<table><caption>**Table 11.** Collision induced absorption (CIA) data sets available from HITRANonline with the wavenumber and temperature ranges for which they provide coverage, and the citations for the original sources.</caption><thead><tr><th>System</th><th>ν<sub>range</sub>/cm<sup>-1</sup></th><th>λ<sub>range</sub>/µm</th><th>T<sub>min</sub>/K</th><th>T<sub>max</sub>/K</th><th>Reference</th></tr></thead><tbody><tr><td>H<sub>2</sub>-CH<sub>4</sub> (equilibrium)</td><td>0–1946</td><td>> 5.14</td><td>40</td><td>400</td><td>Borysow &amp; Frommhold (1986b)</td></tr><tr><td>H<sub>2</sub>-CH<sub>4</sub> (normal)</td><td>0–1946</td><td>> 5.14</td><td>40</td><td>400</td><td>Borysow &amp; Frommhold (1986b)</td></tr><tr><td>H<sub>2</sub>-H<sub>2</sub></td><td>20–10 000</td><td>1–500</td><td>200</td><td>3000</td><td>Abel et al. (2011), Fletcher et al. (2018)</td></tr><tr><td>H<sub>2</sub>-H</td><td>100–11 000</td><td>0.9–100</td><td>1000</td><td>2500</td><td>Gustafsson &amp; Frommhold (2003)</td></tr><tr><td>H<sub>2</sub>-He</td><td>20–20 000</td><td>0.5–500</td><td>200</td><td>9900</td><td>Abel et al. (2012)</td></tr><tr><td>He-H</td><td>50–11 000</td><td>0.9–200</td><td>1500</td><td>10 000</td><td>Gustafsson &amp; Frommhold (2001)</td></tr><tr><td>N<sub>2</sub>-H<sub>2</sub></td><td>0–1886</td><td>> 5.3</td><td>40</td><td>400</td><td>Borysow &amp; Frommhold (1986a)</td></tr><tr><td>N<sub>2</sub>-He</td><td>1–1000</td><td>10–10 000</td><td>300</td><td>300</td><td>Bar-Ziv &amp; Weiss (1972)</td></tr><tr><td>N<sub>2</sub>-N<sub>2</sub></td><td>0–5000</td><td>> 2</td><td>70</td><td>330</td><td>Karman et al. (2015), Baranov, Lafferty &amp; Fraser (2005)<br>Lafferty et al. (1996), Hartmann, Boulet &amp; Toon (2017)<br>Sung et al. (2016a), Chistikov et al. (2019)</td></tr><tr><td>N<sub>2</sub>-air</td><td>1850–5000</td><td>2–5.4</td><td>228</td><td>330</td><td>Baranov et al. (2005), Lafferty et al. (1996)<br>Hartmann et al. (2017), Menoux et al. (1993)</td></tr><tr><td>N<sub>2</sub>-H<sub>2</sub>O</td><td>1930–2830</td><td>3.5–5.2</td><td>250</td><td>350</td><td>Hartmann et al. (2018a)</td></tr><tr><td>O<sub>2</sub>-CO<sub>2</sub></td><td>12 600–13 839</td><td>0.7–0.8</td><td>296</td><td>296</td><td>Vangvichith, Tran &amp; Hartmann (2009)</td></tr><tr><td>O<sub>2</sub>-N<sub>2</sub></td><td>1300–13 840</td><td>0.7–7.7</td><td>193</td><td>296</td><td>Baranov et al. (2005), Lafferty et al. (1996)<br>Thibault et al. (1997), Orlando et al. (1991)<br>Menoux et al. (1993), Maté et al. (1999)<br>Tran, Boulet &amp; Hartmann (2006), Karman et al. (2018)</td></tr><tr><td>O<sub>2</sub>-O<sub>2</sub></td><td>1150–29 800</td><td>0.3–8.7</td><td>193</td><td>293</td><td>Baranov et al. (2004), Maté et al. (1999)<br>Thibault et al. (1997), Orlando et al. (1991)<br>Tran et al. (2006), Karman et al. (2018)<br>Tran et al. (2006), Karman et al. (2018)<br>Spiering &amp; van der Zande (2012), Spiering et al. (2011)<br>Thalman &amp; Volkamer (2013), Kassi et al. (2021)<br>Adkins et al. (2023)</td></tr><tr><td>O<sub>2</sub>-air</td><td>1300–13 839</td><td>0.7–7.7</td><td>193</td><td>300</td><td>Maté et al. (1999), Spiering &amp; van der Zande (2012)<br>Thibault et al. (1997), Orlando et al. (1991)<br>Tran et al. (2006), Karman et al. (2018)<br>Drouin et al. (2017)</td></tr><tr><td>CO<sub>2</sub>-CO<sub>2</sub></td><td>1–3250</td><td>3.1–10 000</td><td>200</td><td>298</td><td>Gruszka &amp; Borysow (1997), Baranov &amp; Vigasin (1999)<br>Baranov (2018)</td></tr><tr><td>CO<sub>2</sub>-H<sub>2</sub></td><td>0–2000</td><td>> 5</td><td>200</td><td>350</td><td>Wordsworth et al. (2017)</td></tr><tr><td>CO<sub>2</sub>-He</td><td>0–1000</td><td>> 10</td><td>300</td><td>300</td><td>Bar-Ziv &amp; Weiss (1972)</td></tr><tr><td>CO<sub>2</sub>-CH<sub>4</sub></td><td>1–2000</td><td>5–10 000</td><td>200</td><td>350</td><td>Wordsworth et al. (2017)</td></tr><tr><td>CO<sub>2</sub>-Ar</td><td>0–300</td><td>> 33</td><td>200</td><td>400</td><td>Turbet et al. (2020)<br>Odintsova et al. (2021)</td></tr><tr><td>CH<sub>4</sub>-He</td><td>1–1000</td><td>> 10</td><td>40</td><td>350</td><td>Taylor, Borysow &amp; Frommhold (1988)</td></tr><tr><td>CH<sub>4</sub>-Ar</td><td>1–697</td><td>14.3–10 000</td><td>70</td><td>296</td><td>Samuelson, Nath &amp; Borysow (1997)</td></tr><tr><td>CH<sub>4</sub>-N<sub>2</sub></td><td>0–1379</td><td>> 7.3</td><td>40</td><td>400</td><td>Finenko et al. (2021, 2022)</td></tr><tr><td>CH<sub>4</sub>-CH<sub>4</sub></td><td>0–990</td><td>> 10.1</td><td>200</td><td>800</td><td>Borysow &amp; Frommhold (1987)</td></tr></tbody></table>

## 5.4 Data used by retrieval codes

### 5.4.1 Collision induced absorption (CIA)

CIA data are readily usable in retrieval codes such as ARCIS, TauREx3, petitRADTRANS, and others mentioned in Table 2. The HITRAN formatted data (.cia) provides a basis for including CIA data<fnref n="36" /> (Karman et al. 2019). As noted above, CIA including H₂ and He is commonly included in the atmospheric modelling of hydrogen and helium rich atmospheres and the corresponding data are distributed with the default data sets for retrieval codes such as TauREx3 and ARCIS. Additional species can be included in the modelling and retrieval codes as they become available or are updated, following the HITRAN format to maximize interoperability. Though data format does not pose a major limitation, restricted

wavelength range coverage for the CIA data poses constraints on modelling where CIA between additional species is considered.

The HITRAN CIA format supports different sets of absorption data for the same pair of species but for varying temperatures. While this is provided in some data sets, it relies on implementation in the retrieval code or downstream processing library in order to query based on temperature. This is not always supported.

### 5.4.2 Continuum

Anisman et al. (2022a) modelled the effects of including versus not including continuum data for H₂O on examples of low-mass temperate exoplanet transmission spectra using TauREx3 (Al-Refaie et al. 2021). The effect of continuum cross-sections exhibit an inverse relationship with temperature, so are most important for use in modelling atmospheres with temperatures at or close to Earth's.

As previously mentioned, one aspect of using continuum models such as MT_CKD is that they rely on specific line wings being

<footnote n="36">³⁶https://hitran.org/data/CIA/CIA.Readme.pdf</footnote>
sociated GitHub page for the LX-MIE mie-scattering tool<fnref n="44" />
Similarly, the wavelength-dependent refractive indices used
in Wakeford & Sing (2015) can be downloaded from the
author's webpage<fnref n="45" />, although some updates have become
available since this early compilation.

Anisotropic crystalline particles have refractive indices
which depend on the direction of propagation of incident
light with respect to the particle's axes of symmetry. The
complex refractive indices related to light perpendicular to
the main symmetry axis are often named ordinary, while
the others are labelled extraordinary. These complex indices
should typically be averaged over the three (x, y, z) axes of
the particle for use in atmospheric codes. It is not always
clear if available indices have already been mixed, but they
may be labelled either ordinary/extraordinary, or by their x,
y, z axes. Where indices have been averaged is often noted
in compilations such as Kitzmann & Heng (2017) (see their
Table 1).

The Granada-Amsterdam light scattering database<fnref n="46" /> (Muñoz et al. 2012) contains experimental scattering matrices as functions of the scattering angle of samples of small irregular particles. These are laboratory experiments typically performed at two optical wavelengths. Laboratory data such as these are important to be able to benchmark theoretical codes, particularly those which model the scattering matrices of irregularly shaped particles. Available codes for computing complex dust particle opacities, both for spherical and fractal shapes, include optool (Dominik et al. 2021)<fnref n="47" />, the discrete-dipole-approximation code ADDA (Yurkin & Hoekstra 2011)<fnref n="48" />, the T-matrix codes (Mackowski & Mishchenko 1996)<fnref n="49" /> and CORAL (Lodge et al. 2023)<fnref n="50" />. Other well-used codes include PyMieScatt<fnref n="51" /> (Sumlin et al. 2018), which can deal with both direct and inverse Mie calculations.

6.1.2 Thermochemical data

Thermochemical data is required as input to kinetic cloud
models of exoplanet atmospheres, for example in the com-
putation of bulk growth processes for a particular species
as a function of pressure and temperature, which requires
gas-surface reactions (Helling 2021). There are several ther-
mochemical properties of interest for astrophysical studies,
including the Gibbs free energies of formation, the change
in enthalpy, or the entropy (Lecoq-Molinos et al. 2024).
Chemical and cluster data for all the species that take part
in the cloud formation process are required for complete
atmospheric models. Particularly, cluster data is crucial to
model the nucleation process and the formation of cloud
condensation nuclei (CCN). Databases, including the NIST-
JANAF Thermochemical tables (Chase 1998)<fnref n="56" /> and the other

databases listed in Section 7.1.1 contain thermochemical data
but rarely cluster data. As noted by Helling (2021), labora-
tory data are often not available, and so thermochemical data
for various species can also be computed. For example, ther-
mochemical data for TiO₂ nanoclusters have been recently
computed by Sindel et al. (2022a,b), and for VₓOᵧ clusters
by Lecoq-Molinos et al. (2024). These data are appropriate
for use in modelling the formation of cloud condensation
nuclei in exoplanet atmospheres. A variety of other sources
for thermochemical data exist. For example, for TiO₂ data is
provided by Jeong et al. (2000); Lee et al. (2015); Sindel et al.
(2022b), with Jeong et al. (2000) also providing information
about other intermediate TiₓOᵧ structures, including their
vibrational frequencies. For SiO and SiO₂ thermochemical
data are available from works such as Bromley et al. (2016);
Flint & Fortenberry (2023). For MgO, Köhler et al. (1997)
provide thermochemical data, and Chen et al. (2014) provide
cluster structures and vibrational spectra for (MgO)ₓ (but not
thermochemical data directly). Other species with thermo-
chemical data include silicon carbide (Gobrecht et al. 2017),
KCl (Rose & Berry 1992), silicates (Goumans & Bromley
2012), magnesium and calcium aluminates (Gobrecht et al.
2023), aluminium oxides (Gobrecht et al. 2022, 2018; Lam
et al. 2015; Patzer et al. 2005), and Fe-bearing species (Chang
et al. 2013; Ding et al. 2009).

Condensation curves (or vapour pressure curves), which
indicate the thermal stability of a species, are necessary in-
puts into codes which predict the expected cloud materials
present in an exoplanet’s atmosphere based on it’s pressure
and temperature structure, typically found via global climate
models (GCMs). Condensation curves appropriate for mod-
elling exoplanet atmospheres up to high temperatures are
available from sources such as Gao et al. (2020)<fnref n="57" />, Lodders
(2003), Woitke et al. (2018).

6.1.3 Measured cloud and haze production in the laboratory

Atmospheric chamber experiments can be used to simulate
the atmospheres of Solar System bodies, in order to study
haze formation, and measure transmittance and reflectance
spectra. This has been done for Titan (He et al. 2022a;
Imanaka et al. 2004), Triton (Moran et al. 2022), and early
Earth (DeWitt et al. 2009; Hörst et al. 2018b); in such stud-
ies many haze properties can be measured, such as produc-
tion rate, bulk composition, molecular composition, and their
transmission and reflectance from the optical to the near-
infrared. The latter is done with Fourier Transform Infrared
(FTIR) Spectroscopy. In the case of He et al. (2022a), they
provide a set of optical constants of Titan haze analogs in the
wavelength range 0.4 - 3.5 µm. Similar atmospheric chamber
experiments have been used to study the formation of water
ice clouds on Mars (Phebus et al. 2011), the reflectance spec-
trum of Phobos from 0.4 - 4.75 µm (Wargnier et al. 2023),
organic aerosols in anoxic and oxic atmospheres of Earth-like
exoplanets (Gavilan et al. 2018), precursors to haze forma-
tion in cool exoplanet atmospheres (He et al. 2019), and to
explore haze production in super-Earth and mini-Neptune
atmospheres (Hörst et al. 2018a).

<footnote n="57">⁵⁷ https://github.com/natashabatalha/virga/blob/
master/virga/pvaps.py</footnote>

<footnote n="44">⁴⁴ https://github.com/exoclime/LX-MIE</footnote>

<footnote n="45">⁴⁵ https://stellarplanet.org/science/condensates/</footnote>

<footnote n="46">⁴⁶ https://www.iaa.csic.es/scattering/list/index.html</footnote>

<footnote n="47">⁴⁷ https://github.com/cdominik/optool.git</footnote>

<footnote n="48">⁴⁸ https://github.com/adda-team/adda.git</footnote>

<footnote n="49">⁴⁹ https://www.giss.nasa.gov/staff/mmishchenko/tmatrix/</footnote>

<footnote n="50">⁵⁰ https://github.com/mglodge/CORAL.git</footnote>

<footnote n="51">⁵¹ https://pymiescatt.readthedocs.io/en/latest/</footnote>

<footnote n="56">⁵⁶ https://janaf.nist.gov/</footnote>
used for computations of corresponding molecular cross-sections. For example, the MT_CKD model assumes that a line wing cutoff of 25 cm⁻¹ is used, otherwise there can be double counting of the wing contribution (see for example, Gharib-Nezhad et al. 2024).

## 5.5 What's being worked on?

CIA coefficients of H₂ and H₂-He have been recently measured in the 3600–5500 cm⁻¹ (~1.8–2.8 µm) spectral range in order to characterize Jupiter's atmosphere (Vitali et al., in preparation). These are shown in Fig. 4, covering pressure values from 10 mbar to 70 bar and temperature values of 120–500 K. It is important to have up-to-date CIA measurements, even for H₂-H₂ and H₂-He where there is already data available (see Table 11), either to expand the wavelength region or to compare the newer measurements to previous measurements or theoretical models present in the literature. As was highlighted in Section 5.1, the CIA of H₂-H₂ and H₂-He contribute significantly to the opacity of Jupiter, and therefore Jupiter-like exoplanets (Trafton 1964; Borysow et al. 1984; Richard et al. 2012).

CIA laboratory measurements have also been published recently from groups in Grenoble (Fleurbay et al. 2022). There is a simulation chamber for absorption spectroscopy in planetary atmospheres in INAF (Snels et al. 2021); see Fig. 5. In terms of species, molecular systems related to Venus and early Mars are mostly being targeted, i.e. CO₂-H₂O and CO₂-CO₂.

Theoretical work is planned for molecular systems related to Jupiter-like planets.

## 5.6 Data needs: what's missing and urgent?

### 5.6.1 Collision induced absorption (CIA)

(i) **Data for additional CIA species:** CIA data are currently unavailable for many pairs of species that are thought to be important in exoplanetary atmospheres, for example N₂ is currently the only available species for collisions with H₂O (see Table 11). H₂O, and other species such as CO₂ and CH₄ have been detected multiple times in exoplanet atmosphere spectra with JWST. CIA data for these molecules or combinations including them is crucial for a correct interpretation of the spectra especially in the search for biosignatures (Malaterre et al. 2023).

(ii) **Wavelength coverage:** much of the data that is available is limited to bands at wavelength ranges outside the window observed by Ariel (see Table 11 for an overview of current coverage of CIA data). The broad CIA features will, however, impact the shape of the Ariel observed spectra. In Jupiter's spectra, some CIA contributions are visible between 2 and 2.5 microns (López-Puertas et al. 2018) which is lower in wavelength than the data available for many other pairs. Without taking CIA into account, it is possible that major uncertainties on the retrieved values will remain.

(iii) **Temperature dependence:** data are often available only at room temperature, and the extension to higher temperatures is desirable, especially for modelling hot Jupiters and lava planets. We note some work has been done towards this, e.g. Stefani et al. (2018) for CO₂, along with data for H₂-H₂, H₂-H, H₂-He, He-H, detailed in Table 11.

(iv) **Isotopologues:** CIA data coverage does not currently extend far in the consideration of isotopologues; generally the main isotopologue is taken for theoretically derived data, while the terrestrial ratios are used for experimentally derived data. The potential impact of a variation in isotopologues for CIA is small, meaning isotopologue

data are less urgent than for the above mentioned pairs, though it is potentially relevant for the HD-H₂ pair (Karman et al. 2019).

### 5.6.2 Continuum

(i) **Species other than H₂O:** although the MT_CKD data base contains some other species such as CO₂, O₃, N₂, and O₂, the main focus of continuum absorption has been on H₂O. (Exo)planets with atmospheres dominated by species other than H₂O have a particular need for continuum data.

(ii) **Temperature dependence:** continuum absorption data for H₂O is currently only available below temperatures of 500 K, so there is no available data to use for modelling exoplanets with higher temperature atmospheres. We do note, however, that the impact of the H₂O continuum absorption on spectra decreases as the temperature increases (Anisman et al. 2022a), so this is not thought to be of urgent concern for the community.

# 6 AEROSOLS AND SURFACES

It is well known that the types of cloud or haze particles, collectively known as aerosols, in the atmospheres of exoplanets will have an effect on their observed transmission or emission spectra (Wakeford & Sing 2015; Pinhas & Madhusudhan 2017; Min et al. 2020; Ma et al. 2023; Taylor & Parmentier 2023), and their albedo (García Muñoz & Isaak 2015; Fraine et al. 2021; Barrientos et al. 2023; Chubb et al. 2023), through the cloud particle's absorption and scattering properties. Generally, haze particles are smaller than cloud particles and are formed via photochemistry, which is a disequilibrium process with stellar UV leading to the formation of solid particles. Clouds, on the other hand, are formed via equilibrium processes which involve condensation and rainout.

Gao et al. (2021) give an overview of the state of knowledge (as of 2021 February) of exoplanet aerosols as determined from observations, modelling, and laboratory experiments. Wavelength-dependent, and ideally also temperature-dependent, refractive indices, also known as optical constants, are required to compute the scattering and absorption coefficients of clouds and hazes (aerosols) for use in exoplanetary atmosphere models. Thermochemical data are also required for predicting which aerosol species are expected to be present at various altitudes and horizontally across a given exoplanet atmosphere. Typically the outputs from global circulation models (GCMs), such as pressure-temperature profiles as a function of longitude and latitude across the planet (see e.g. Cho, Skinner & Thrastarson 2021; Lee et al. 2021; Schneider et al. 2022; Skinner & Cho 2022; Skinner, Nättilä & Cho 2023; Changeat et al. 2024, for details on GCMs and complex atmospheric dynamics) are used as a basis for kinetic cloud models which then predict which particles will condense at different locations of the atmosphere (see e.g. Ackerman & Marley 2001; Helling et al. 2008; Gao et al. 2020a; Roman et al. 2021; Rooney et al. 2022; Helling et al. 2023). Usually some assumptions need to be made, such as initial elemental abundances available for condensing, and the strength of vertical atmospheric mixing, for example. Due to the importance of optical and UV data for understanding aerosol properties in exoplanet atmospheres, it has been suggested by Fairman et al. (2024) and Wakeford et al. (2020) that one should complement JWST observations with one from HST.

Similarly, planetary surfaces can be modelled by a simple reflectance model (for instance using linear mixing) (Cui et al. 2018), but also by radiative transfer modelling using optical constants, similar to the approach for Solar System planets (Hapke 1993; Andrieu
<table><caption>**Table 11.** Sources used for the real (n) and imaginary (k) parts of the refractive indices of a selection of species of various materials typically used in (exo)planetary atmosphere models of clouds and hazes (aerosols). The temperature range is left blank in cases where we assume the data is not higher than room temperature.</caption><thead><tr><th>Material</th><th>Form</th><th>Name</th><th>Wavelength range (μm)</th><th>Temperature range (K)</th><th>Reference</th></tr></thead><tbody><tr><td>Al<sub>2</sub>O<sub>3</sub></td><td>Crystalline</td><td>Corundum</td><td>0.03 - 10</td><td></td><td>Palik (2012)</td></tr><tr><td>Al<sub>2</sub>O<sub>3</sub></td><td>Crystalline</td><td>Corundum</td><td>6.7 - 10,000</td><td>300 - 928</td><td>Zeidler et al. (2013)<sup>a</sup></td></tr><tr><td>Al<sub>2</sub>O<sub>3</sub></td><td>Amorphous</td><td>Corundum</td><td>0.2 - 12</td><td></td><td>Koike et al. (1995)<sup>a</sup></td></tr><tr><td>Al<sub>2</sub>O<sub>3</sub></td><td>Amorphous</td><td>Corundum</td><td>7.8 - 500</td><td></td><td>Begemann et al. (1997)<sup>a</sup></td></tr><tr><td>C<sub>x</sub>H<sub>y</sub>N<sub>z</sub></td><td>Solid</td><td>Tholins</td><td>0.02 - 920</td><td></td><td>Khare et al. (1984a)<sup>b</sup></td></tr><tr><td>KCl</td><td>Crystalline</td><td>Potassium chloride</td><td>0.22 - 167</td><td></td><td>Querry & Wieliczka (1987)</td></tr><tr><td>Fe</td><td>Metallic</td><td>Iron</td><td>0.1 - 100,000</td><td></td><td>Palik (2012)</td></tr><tr><td>Fe<sub>2</sub>O<sub>3</sub></td><td>Solid</td><td>Hematite</td><td>0.1 - 1000</td><td></td><td>Triaud (2005)<sup>d</sup></td></tr><tr><td>Fe<sub>2</sub>SiO<sub>4</sub></td><td>Crystalline</td><td>Fayalite</td><td>0.4 - 10,000</td><td></td><td>Unpublished<sup>a</sup></td></tr><tr><td>FeO</td><td>Amorphous</td><td>Wustite</td><td>10 - 510</td><td>10 - 300</td><td>Henning et al. (1995)</td></tr><tr><td>FeS</td><td>Crystalline</td><td>Troilite</td><td>20 - 480</td><td>10 - 300</td><td>Pollack et al. (1994)</td></tr><tr><td>MgAl<sub>2</sub>O<sub>4</sub></td><td>Crystalline</td><td>Spinel</td><td>6.7 - 10,000</td><td>300 - 928</td><td>Zeidler et al. (2013)<sup>a</sup></td></tr><tr><td>Mg<sub>2</sub>SiO<sub>4</sub></td><td>Amorphous</td><td>Forsterite</td><td>0.2 - 950</td><td></td><td>Jäger et al. (2003)</td></tr><tr><td>Mg<sub>2</sub>SiO<sub>4</sub></td><td>Crystalline</td><td>Forsterite</td><td>2 - 100</td><td>50 - 295</td><td>Suto et al. (2006)</td></tr><tr><td>MgO</td><td>Cubic</td><td>Magnesium oxide</td><td>0.2 - 625</td><td></td><td>Palik (2012)</td></tr><tr><td>MgSiO<sub>3</sub></td><td>Amorphous (glass)</td><td>Enstatite</td><td>0.2 - 500</td><td></td><td>Dorschner et al. (1995)<sup>e</sup></td></tr><tr><td>MgSiO<sub>3</sub></td><td>Amorphous (sol-gel)</td><td>Enstatite</td><td>0.2 - 1000</td><td></td><td>Jäger et al. (2003)<sup>e</sup></td></tr><tr><td>MgSiO<sub>3</sub></td><td>Crystalline</td><td>Enstatite</td><td>2 - 98</td><td></td><td>Jaeger et al. (1998)</td></tr><tr><td>SiO<sub>2</sub></td><td>Crystalline</td><td>Quartz</td><td>0.05 - 8.4</td><td></td><td>Palik (2012)</td></tr><tr><td>SiO<sub>2</sub></td><td>Crystalline</td><td>Quartz</td><td>6.25 - 10,000</td><td>300 - 928</td><td>Zeidler et al. (2013)</td></tr><tr><td>SiO</td><td>Amorphous</td><td>Silicon oxide</td><td>0.05 - 100</td><td></td><td>Palik (2012)</td></tr><tr><td>TiO<sub>2</sub></td><td>Crystalline</td><td>Rutile</td><td>0.4 - 10</td><td></td><td>Zeidler et al. (2011)</td></tr><tr><td>TiO<sub>2</sub></td><td>Crystalline</td><td>Anatase</td><td>0.4 - 10</td><td></td><td>Zeidler et al. (2011)</td></tr></tbody></table>

### 6.1.4 Surface reflectance

For terrestrial exoplanets and exomoons, the bottom boundary layer of the atmosphere must be considered as the surface. Table 12 summarises the most important databases of surface reflectance in Planetary Science. They encompass Earth natural rocks analogues, but also synthetic materials in the lab (such as ices), meteorites and in-situ samples measured back in Earth, such as the Moon or Ryugu asteroid. The dataset from in-situ measurements of reflectance in planetary science is usually scattered and unfortunately, such a database is missing.

Reflectance measurements can be done over several wavelength ranges, but also for different observation geometries (incidence and emergence angles), leading to Bidirectional Reflectance Distribution Function (BRDF). Laboratory measurements can also be done for various surface properties (such as grain size, grain shape, compaction, roughness) but also various kinds of mixtures (such as linear geographic mixture, intimate mixture, intra-grain mixture).

The databases which contain information on observation geometries and the nature of the samples do not always contain

optical constants, as can be seen by Table 12. Additionally some databases contain characterisation of the samples using other methods of investigations (Raman spectroscopy, petrological description, X-ray diffraction, Laser Induced Breakdown Spectroscopy, X-ray fluorescence or electron microscope analysis). The SSHADE (Solid Spectroscopy Hosting Architecture of Databases and Expertise) (Schmitt et al. 2018) database regroups several databases. It provides spectral and photometric data obtained by various spectroscopic techniques including reflectance spectroscopy. The measured samples include ices, pure minerals, rocks, organic and carbonaceous materials as well as liquids. They are either synthetic, natural samples from Earth or other planetary bodies such as (micro-)meteorites or lunar soils. Additional characterizations of the samples are not systematically done but optical constants are often available and observation geometries are generally given. The USGS-Speclab (Kokaly et al. 2017) database regroups spectra measured with laboratory, field, and airborne spectrometers, acquired on samples of specific minerals, plants, chemical compounds, and man-made materials. It also contains mathematically computed mixtures. The RELAB database regroups spectroscopic data

<footnote>a: Available via the Database of Optical Constants for Cosmic Dust⁵²</footnote>

<footnote>b: Available via the HITRAN2020 database⁵³ (Gordon et al. 2022)</footnote>

<footnote>c: Available via the refractiveindex.info database of optical constants⁵⁴ (Polyanskiy 2024)</footnote>

<footnote>d: Available via the Aerosol Refractive Index Archive (ARIA)⁵⁵</footnote>

<footnote>e: Jäger et al. (2003) use the sol-gel method, and Dorschner et al. (1995) a melting and quenching technique; a comparison between the two is made in Jäger et al. (2003).</footnote>
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Left: H₂ CIA coefficients at different pressures (8, 10, 12 bars) and room temperature (296 K). Right: H₂-He CIA coefficients at different pressures (10, 13 bars)) and temperatures (201, 299, 401 K). (Vitali et al., in preparation).</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 5.** The absorption spectra of 16.03 amagat of carbon dioxide at 243 and 323 K, demonstrating a number of CIA bands, recorded with PASSxS, after subtraction of the strong contributions of the $v_3$ fundamental band at 2350 cm⁻¹ and a weaker Fermi doublet ($v_3 + v_1$, $v_3 + 2v_2$) around 3700 cm⁻¹, as well as contributions of the ¹⁶O¹²C¹⁸O isotopomer (Snels et al. 2021).</figcaption></figure>

et al. 2015). In both contexts, already existing data bases contain relevant and useful sources but in many cases new measurements would be desirable (see Section 6.4). Here we outline some typical sources of currently available data for these different applications.

## 6.1 State-of-the-art – data availability

### 6.1.1 Refractive indices

Codes which compute the scattering and absorption properties of a particle or a surface require the refractive index to be known. The real and imaginary part of the refractive index relates to the scattering and absorption properties of the material, respectively. The refractive indices of aersols are complex numbers which are intrinsically dependent on the individual compositions of the aerosols (see for example, Di Biagio et al. 2019). If modelling an atmospheric layer consisting of different types of aerosol materials, or modelling mixed-material aerosol particles, then the refractive indices are often

mixed using effective medium theory (see for example, Mishchenko, Dlugach & Liu 2016).

As well as there being variation with wavelength, there is also some temperature dependence with refractive indices. This has been demonstrated by Zeidler, Mutschke & Posch (2015) for olivine and enstatite up to 973 K and by Zeidler, Posch & Mutschke (2013) for corundum, spinel, and α-quartz up to 900 K. It can be seen by such studies that the vibrational band peaks shift slightly and their shapes change with temperature, which could be enough to lead to some incorrect conclusions when trying to fit models to observed spectra. The reason for these changes is that at higher temperatures, vibrational transitions will tend to occur between higher states with slightly lower energetic differences, and the states will be broadened by vibrational mode interactions (Zeidler et al. 2015). This causes broader and redshifted bands with increasing temperature; see for example, figs 2–7 of Zeidler et al. (2015). We note that data for high temperatures are typically not currently available for many species, and so room temperature data are often used for characterizing higher temperature planetary atmospheres, potentially causing biases in analyses.

Table 12 gives the wavelength and temperature range of the available refractive indices of various materials from different sources. We note this table is not exhaustive, and it is useful to search in the various data bases and compilations mentioned below for additional species of interest. Studies which predict which cloud or haze species are present in various exoplanet atmospheres (see for example, Ackerman & Marley 2001; Helling et al. 2008; Gao et al. 2020a; Rooney et al. 2022; Helling et al. 2023) are useful as references for species to include in atmospheric models.

Data bases which compile the optical properties of various materials include:

**Heidelberg-Jena-St.Petersburg-Data base of Optical Constants (HJPDOC)**<fnref n="41" /> (Henning et al. 1999; Jäger et al. 2003) and **Data base of Optical Constants for Cosmic Dust**<fnref n="42" /> provide optical properties (refractive index, reflectivity, transmittance) of analogue materials of cosmic dust in the wavelength range from the UV to

<footnote n="41">⁴¹https://www2.mpia-hd.mpg.de/HJPDOC/</footnote>

<footnote n="42">⁴²https://www.astro.uni-jena.de/Laboratory/OCDB/</footnote>
acquired on pure minerals, natural and synthetic samples. The ECOSTRESS database regroups data acquired on natural and man-made materials. The Mineral and Rock Sample Database University of Winnipeg contains data acquired on pure minerals and natural or synthetic mixtures. Finally, the PTAL (Planetary Terrestrial Analogue Library, Dypvik et al. 2021) regroups data acquired on whole rocks. Additional characterisation are systematically done by multiple techniques.

## 6.2 Data used by retrieval codes

There are a number of exoplanetary atmospheric modelling and retrieval codes which include the effects of solid-state aerosol species in their radiative transfer calculations, and thus require wavelength-dependent, and ideally also temperature-dependent, refractive index data (e.g. Wakeford & Sing 2015; Pinhas & Madhusudhan 2017; Min et al. 2020; Ma et al. 2023; Taylor & Parmentier 2023). The LRS instrument onboard JWST, covering ~5-12.5 µm, has allowed for observations of vibrational-mode aerosol signatures which are commonly found in this wavelength region; for example quartz clouds were inferred in hot Jupiter WASP-17 b by Grant et al. (2023) (using optical constants of crystalline SiO₂ at 928 K from Zeidler et al. 2013) and silicate clouds (MgSiO₃, SiO₂ and SiO) have been inferred in hot Neptune WASP-107 b by Dyrek et al. (2024).

Many retrieval codes have their own built-in library of refractive indices, the option to directly include pre-computed cloud opacities, or both. See for example Table 1 of Min et al. (2020) for species included in atmospheric modelling and retrieval code ARCIS, with the option to read in your own refractive index data and use the code to compute either spherical or irregularly shaped particles (Min et al. 2005) of a defined particle size distribution. petitRADTRANS (Mollière et al. 2019) also has a list of built-in cloud opacities<fnref n="58" />, with the option to add more. There are also available add-ons which can be coupled with existing retrieval codes. For example, YunMa is an exoplanet cloud simulation and retrieval package (Ma et al. 2023) which can be coupled to the TauREx3 platform to retrieve microphysical cloud properties in exoplanet atmospheres.

Typically in cases of databases and compilations, the original citation for the measured or computed refractive indices is mentioned. It is important these citations are also included in any atmospheric modelling or retrieval works, as there may be future updates for particular species. Works such as Zeidler et al. (2015) highlight the importance of this; they show temperature-dependence has an impact on the refractive indices and therefore the spectral features which are included in atmospheric models for enstatite and olivine.

The refractive indices determined by using different techniques can differ from one another; for example in the case of amorphous MgSiO₃ (see Table 11) Jäger et al. (2003) use the sol-gel method, and Dorschner et al. (1995) use melting and quenching technique; a comparison between the two is made in Figure 4 of Jäger et al. (2003).

Surfaces are often neglected but some retrievals propose to include surface reflectance measurements by linear mixing (Cui et al. 2018). More advanced radiative transfer mod-

elling will be used, based on optical constants, for instance using the Hapke model (Hapke 1993). Surface reflection is also an important component of studies such as Stam (2008); Fauchez et al. (2017); Trees & Stam (2022), who consider the reflection spectra of a planet's atmosphere and surface (if there is one), including polarisation state; these studies are particularly useful for modelling the observations of future instruments such as the Habitable Worlds Observatory (HWO) (Vaughan et al. 2023).

## 6.3 What's being worked on?

Ongoing laboratory experiments (see, for example, setups described in works such as Deguine et al. 2020; Drant et al. 2024; He et al. 2024) are essential for measuring new refractive indices, exploring photochemical pathways, and verifying theoretical calculations related to aerosols in planetary and exoplanet atmospheres. He et al. (2024), for example, recently measured the density and optical properties of organic haze analogues generated in water-rich exoplanet atmosphere experiments, for use in characterising observations from JWST. Gao et al. (2021), who give an overview of the state of knowledge (as of February 2021) of aerosols for exoplanet atmospheres, highlight the importance of ongoing laboratory experiments and development of theoretical methods. A recent review by Sciamma-O'Brien et al. (2024) further highlights the importance of measured optical constants of laboratory aerosols for interpreting JWST observations.

## 6.4 Data needs: What's missing and urgent?

(i) **Wavelength coverage:** as shown in Table 11, the available data for a number of species do not have the wavelength-coverage required to cover the observational range of Ariel (0.5 - 7.8 µm) and/or other telescopes related to exoplanet atmospheres such as JWST (~0.6 - 28.5 µm, with the LRS instrument covering ~5 - 13 µm). We also note that refractive indices are important for modelling reflectance spectra of exoplanets and their atmospheres, including albedo in the visible region (see, for example, Krenn et al. 2023; Chubb et al. 2023).

(ii) **Temperature-dependence:** although there have been studies such as Zeidler et al. (2015) which demonstrate the temperature-dependence of optical properties (i.e. refractive indices) of aerosol particles such as enstatite and olivine which are expected in typical hot gas giant exoplanets, the vast majority of refractive indices available are for terrestrial temperatures. The main spectral features of such species which could be observed in exoplanet atmospheres using JWST, for example, may be shifted at high-temperatures in comparison to room-temperature. Reflectance spectra and refractive indices above 500 - 600 K are also important for surfaces, but at such high temperatures are usually not available.

(iii) **A guide on what tools to use:** computing cloud opacities from refractive index data requires some additional assumptions, including the shape of the cloud particles, and whether the particles are mixed composition. For example, Mie computations are fast but only applicable for spherical particles, while the discrete dipole approximation (DDA) (Draine & Flatau 1994) or T-matrix method (Mackowski & Mishchenko 1996) are very accurate for non-spherical particles which can be composed of multiple materials, but also computationally expensive. There are

<footnote n="58">⁵⁸ https://petitradtrans.readthedocs.io/en/latest/content/available_opacities.html</footnote>
<table><caption>**Table 12.** Sources used for the real (n) and imaginary (k) parts of the refractive indices of a selection of species of various materials typically used in (exo)planetary atmosphere models of clouds and hazes (aerosols). The temperature range is left blank in cases where we assume the data are not higher than room temperature.</caption><thead><tr><th>Material</th><th>Form</th><th>Name</th><th>Wavelength range (µm)</th><th>Temperature range (K)</th><th>Reference</th></tr></thead><tbody><tr><td>Al<sub>2</sub>O<sub>3</sub></td><td>Crystalline</td><td>Corundum</td><td>0.03–10</td><td>–</td><td>Palik (2012)</td></tr><tr><td>Al<sub>2</sub>O<sub>3</sub></td><td>Crystalline</td><td>Corundum</td><td>6.7–10 000</td><td>300–928</td><td>Zeidler et al. (2013)<sup>a</sup></td></tr><tr><td>Al<sub>2</sub>O<sub>3</sub></td><td>Amorphous</td><td>Corundum</td><td>0.2–12</td><td>–</td><td>Koike et al. (1995)<sup>a</sup></td></tr><tr><td>Al<sub>2</sub>O<sub>3</sub></td><td>Amorphous</td><td>Corundum</td><td>7.8–500</td><td>–</td><td>Begemann et al. (1997)<sup>a</sup></td></tr><tr><td>C<sub>x</sub>H<sub>y</sub>N<sub>z</sub></td><td>Solid</td><td>Tholins</td><td>0.02–920</td><td>–</td><td>Khare et al. (1984)<sup>b</sup></td></tr><tr><td>KCl</td><td>Crystalline</td><td>Potassium chloride</td><td>0.22–167</td><td>–</td><td>Querry & Wieliczka (1988)</td></tr><tr><td>Fe</td><td>Metallic</td><td>Iron</td><td>0.1–100 000</td><td>–</td><td>Palik (2012)</td></tr><tr><td>Fe<sub>2</sub>O<sub>3</sub></td><td>Solid</td><td>Hematite</td><td>0.1–1000</td><td>–</td><td>Triaud (2005)<sup>d</sup></td></tr><tr><td>Fe<sub>2</sub>SiO<sub>4</sub></td><td>Crystalline</td><td>Fayalite</td><td>0.4–10 000</td><td>–</td><td>Unpublished<sup>a</sup></td></tr><tr><td>FeO</td><td>Amorphous</td><td>Wustite</td><td>10–510</td><td>10–300</td><td>Henning et al. (1995)</td></tr><tr><td>FeS</td><td>Crystalline</td><td>Troilite</td><td>20–480</td><td>10–300</td><td>Pollack et al. (1994)</td></tr><tr><td>MgAl<sub>2</sub>O<sub>4</sub></td><td>Crystalline</td><td>Spinel</td><td>6.7–10 000</td><td>300–928</td><td>Zeidler et al. (2013)<sup>a</sup></td></tr><tr><td>Mg<sub>2</sub>SiO<sub>4</sub></td><td>Amorphous</td><td>Forsterite</td><td>0.2–950</td><td>–</td><td>Jäger et al. (2003)</td></tr><tr><td>Mg<sub>2</sub>SiO<sub>4</sub></td><td>Crystalline</td><td>Forsterite</td><td>2–100</td><td>50–295</td><td>Suto et al. (2006)</td></tr><tr><td>MgO</td><td>Cubic</td><td>Magnesium oxide</td><td>0.2–625</td><td>–</td><td>Palik (2012)</td></tr><tr><td>MgSiO<sub>3</sub></td><td>Amorphous (glass)</td><td>Enstatite</td><td>0.2–500</td><td>–</td><td>Dorschner et al. (1995)<sup>e</sup></td></tr><tr><td>MgSiO<sub>3</sub></td><td>Amorphous (sol-gel)</td><td>Enstatite</td><td>0.2–1000</td><td>–</td><td>Jäger et al. (2003)<sup>e</sup></td></tr><tr><td>MgSiO<sub>3</sub></td><td>Crystalline</td><td>Enstatite</td><td>2–98</td><td>–</td><td>Jaeger et al. (1998)</td></tr><tr><td>SiO<sub>2</sub></td><td>Crystalline</td><td>Quartz</td><td>0.05–8.4</td><td>–</td><td>Palik (2012)</td></tr><tr><td>SiO<sub>2</sub></td><td>Crystalline</td><td>Quartz</td><td>6.25–10 000</td><td>300–928</td><td>Zeidler et al. (2013)</td></tr><tr><td>SiO</td><td>Amorphous</td><td>Silicon oxide</td><td>0.05–100</td><td>–</td><td>Palik (2012)</td></tr><tr><td>TiO<sub>2</sub></td><td>Crystalline</td><td>Rutile</td><td>0.4–10</td><td>–</td><td>Zeidler et al. (2011)</td></tr><tr><td>TiO<sub>2</sub></td><td>Crystalline</td><td>Anatase</td><td>0.4–10</td><td>–</td><td>Zeidler et al. (2011)</td></tr></tbody></table>

far IR mainly produced in Astrophysical Institute and University Observatory (AIU) Jena but also from open sources, including papers, reports, dissertations, where these data were derived. Users of the data base are requested to cite the original sources of data.

**Aerosol refractive index archive (ARIA)**<sup>43</sup> is a comprehensive collection of refractive index data sets of typical aerosol particles covering broad spectroscopic ranges. It contains both the original experimental and interpolated (mostly from very low to room temperature) values, intended to be used in light scattering parameters by the Oxford Mie Code.<sup>44</sup>

**HITRAN2020 data base**<sup>45</sup> (Gordon et al. 2022) provides refractive indices from visible to millimetre spectral ranges for many types of species including aerosol particles, required for calculation of extinction, scattering, and absorptive properties of atmospheric particles aiming at terrestrial and planetary (low temperature) applications.

**Optical constants data base (OCdb)**<sup>46</sup> (see e.g. Drant et al. 2024) provides optical constants of organic refractory materials (i.e. tholins, laboratory analogues of photochemical hazes) and ices relevant

to planetary and astrophysical environments from peer-reviewed sources. The data provided mostly correspond low temperatures.

**Open-source data base of optical constants**<sup>refractiveindex.info</sup><sup>47</sup> (Polyanskiy 2024) is another recent resource of optical data from publicly available sources such as scientific publications and material data sheets published by manufacturers.

**Texas A&M University dust 2020 data base** (TAMUdust2020) (Saito et al. 2021) compiles the optical properties of irregularly shaped aerosol particles, such as dust aerosol and volcanic ash particles, for a wide range of values of the size parameter, the index of refraction, and the degree of sphericity. The data are produced using state-of-the-art light scattering calculations and validated with laboratory/in-situ measurements.

Original sources of refractive index data include those of works such as Henning et al. (1995), Jäger et al. (2003), Dorschner et al. (1995), Zeidler et al. (2011), and Drant et al. (2024). A number of species are also included in Palik's handbook of optical constants (Palik 2012).

Refractive indices as a function of wavelength for different species thought to be present in exoplanetary atmospheres have been compiled in various works, such as Lavvas & Koskinen (2017),

<footnote><sup>a</sup>: Available via the data base of optical constants for cosmic dust<sup>37</sup></footnote>

<footnote><sup>b</sup>: Available via the HITRAN2020 data base<sup>38</sup> (Gordon et al. 2022)</footnote>

<footnote><sup>c</sup>: Available via the refractiveindex.info data base of optical constants<sup>39</sup> (Polyanskiy 2024)</footnote>

<footnote><sup>d</sup>: Available via the aerosol refractive index archive (ARIA)<sup>40</sup></footnote>

<footnote><sup>e</sup>: Jäger et al. (2003) use the sol-gel method, and Dorschner et al. (1995) a melting and quenching technique; a comparison between the two is made in Jäger et al. (2003).</footnote>

<footnote n="37"><sup>37</sup>https://www.astro.uni-jena.de/Laboratory/OCDB/crsilicates.html</footnote>

<footnote n="38"><sup>38</sup>https://hitran.org/aerosols/</footnote>

<footnote n="39"><sup>39</sup>https://refractiveindex.info/</footnote>

<footnote n="40"><sup>40</sup>http://eodg.atm.ox.ac.uk/ARIA/</footnote>

<footnote n="43"><sup>43</sup>http://eodg.atm.ox.ac.uk/ARIA/</footnote>

<footnote n="44"><sup>44</sup>https://eodg.atm.ox.ac.uk/MIE/</footnote>

<footnote n="45"><sup>45</sup>https://hitran.org/aerosols/</footnote>

<footnote n="46"><sup>46</sup>https://ocdb.smce.nasa.gov</footnote>

<footnote n="47"><sup>47</sup>https://refractiveindex.info/</footnote>
<table><caption>**Table 12.** Sources for the reflectance data of various materials typically used in (exo)planetary surface models.</caption><thead><tr><th>Name</th><th>Reflectance</th><th>Optical constants</th><th>Link</th></tr></thead><tbody><tr><td>SSHADE</td><td>X</td><td>X</td><td>https://www.sshade.eu/</td></tr><tr><td>USGS-Speclab</td><td>X</td><td></td><td>https://crustal.usgs.gov/speclab/</td></tr><tr><td>RELAB</td><td>X</td><td></td><td>https://sites.brown.edu/relab/</td></tr><tr><td>ECOSTRESS</td><td>X</td><td></td><td>https://speclib.jpl.nasa.gov</td></tr><tr><td>Mineral and Rock Sample</td><td>X</td><td></td><td>https://www.uwinnipeg.ca/c-tape/sample-database.html</td></tr><tr><td>Database University of Winnipeg</td><td>X</td><td></td><td>http://erica.uva.es/PTAL/</td></tr><tr><td>PTAL</td><td>X</td><td></td><td></td></tr></tbody></table>

some methods which lie between these extremes, allowing for the computation of scattering and absorption coefficients for non-spherical particle shapes, at a lower cost but typically also lower accuracy that DDA. For example the Distribution of Hollow Spheres (DHS) method (Min et al. 2005) is thought to be a good compromise between accuracy and speed, with the option to choose a degree of irregularity of the particles. The DHS method has been used in works such as Dyrek et al. (2024). There is also the modified mean field theory (MMF) method, designed for computing opacities of dust aggregates (Tazaki & Tanaka 2018). A recent study by Lodge et al. (2023) investigates the differences which result in the use of the Mie, MMF, and DDA approaches, and demonstrates that using a lower resolution for DDA can also be a good compromise in terms of accuracy and speed. There has been a call amongst some users of atmospheric modelling and retrieval codes for some simple tools to create and read-in cloud opacities. There are two approaches here: either the code reads in refractive indices and particle sizes, and uses Mie theory or similar (DHS is available as part of the ARCIS (Min et al. 2020) code, for example) to compute opacities on the fly; or pre-computed grids of opacities for various species, particle sizes, and shapes, can be read in. Codes such as YunMa (Ma et al. 2023) assume spherical particle sizes. For surfaces, simple linear mixing of pure component spectra can be easily surpassed by more advanced radiative transfer models, such as Hapke's (Hapke 1993). The effect of the surface texture (compacity, roughness, grain size) should be also taken into account if the actual data contains sufficient information to retrieve these parameters.

(iv) **Amorphous vs crystalline forms:** there is some discussion in the literature over whether crystalline or amorphous structures are expected under certain situations. It is thought that condensates are typically crystalline if their condensation timescale is faster than their annealing timescale, and amorphous if the opposite is true. This is because in the former case the condensates can rearrange quickly enough to form the crystalline structure, but if the condensation occurs much faster than the formed condensate will be amorphous (Gail & Sedlmayr 1999; Herbort et al. 2020). Grant et al. (2023) demonstrate the impact of choosing either amorphous or crystalline structures for their refractive indices of solid-state SiO₂, with some resulting small differences between their retrieved spectra of WASP-17 b. It is therefore important for refractive indices for both crystalline and amorphous forms to be available for models. Helling (2021) calls for opacity data for condensates across large wavelengths ranges and crystalline species, along with opacity modelling for non-spherical and charged cloud particles.

(v) **Additional species:** it has been noted by members

of the community that sulphuric acid-based aerosols are of interest but are missing from the list of available aerosol refractive indices.

(vi) **Condensation curves:** these are required for all species in order to correctly predict their presence in exoplanet atmospheres, over a large range of pressures and temperatures.

(vii) **In-situ measurements of reflectance:** as noted in this section, these data are available from different sources (see Table 12), but not available in one easily accessible database.

(viii) **Cloud nucleation parameters:** constraints on the surface tension, contact angles and similar parameters are required for the description of cloud nucleation (Arfaux & Lavvas 2024). These are typically used even for non-molecular condensates, which are characterised by the absence of the corresponding condensing species in the gas phase e.g. MgSiO₃ and Na₂S (Arfaux & Lavvas 2024), thus a better understanding of the heterogeneous mechanisms at play for these cases is also needed.

(ix) **Laboratory data to aid understanding of the coupling of haze and clouds:** it is becoming more evident with the latest observations that the coupling of haze and clouds is important (see, for example, Arfaux & Lavvas 2024). To better understand this coupling, experiments that investigate the potential of haze particles acting as nucleation sites for the condensation of anticipated condensates in exoplanet atmospheres are needed.

# 7 ATMOSPHERIC CHEMISTRY

Data required for atmospheric chemistry models appropriate for exoplanet atmospheres primarily include thermochemical data for equilibrium networks, and collision and reaction rates for kinetic networks.

## 7.0.1 Equilibrium chemistry

There are a variety of codes designed to model chemical equilibrium in atmospheres, including exoplanet atmospheres. For example, ACE (Agúndez et al. 2012, 2020), FastChem<fnref n="59" /> (Stock et al. 2018, 2022), and GGchem<fnref n="60" /> (Woitke et al. 2018) all work by minimising the Gibbs free energy of the system for a given pressure and temperature. The minimisation of Gibbs free energy requires

<footnote n="59">⁵⁹ https://github.com/exoclime/FastChem</footnote>

<footnote n="60">⁶⁰ https://github.com/pw31/GGchem</footnote>
Kitzmann & Heng (2017), Min et al. (2020), and Lietzow & Wolf (2022). Kitzmann & Heng (2017) provide a rather extensive compilation of wavelength-dependent refractive indices (real and imaginary components; see their table 1 for species and sources) which are stored at the associated GitHub page for the LX-MIE mie-scattering tool.<fnref n="48" /> Similarly, the wavelength-dependent refractive indices used in Wakeford & Sing (2015) can be downloaded from the author's webpage,<fnref n="49" /> although some updates have become available since this early compilation.

Anisotropic crystalline particles have refractive indices which depend on the direction of propagation of incident light with respect to the particle's axes of symmetry. The complex refractive indices related to light perpendicular to the main symmetry axis are often named ordinary, while the others are labelled extraordinary. These complex indices should typically be averaged over the three (x, y, z) axes of the particle for use in atmospheric codes. It is not always clear if available indices have already been mixed, but they may be labelled either ordinary/extraordinary, or by their x, y, z axes. Where indices have been averaged is often noted in compilations such as Kitzmann & Heng (2017) (see their table 1).

Although the refractive indices data in these data bases exist for very broad spectroscopic ranges, from microwave to far UV, for a given species the coverage is usually rather limited, see for example Table 12. A broad spectral range coverage of the optical properties is crucial to understand the role of aerosols in (exoplanet) atmospheric retrievals via radiative transfer models. In the context of the Ariel spectroscopic range, the IR data are especially urgent, but broader ranges are also required for atmospheric chemistry models involved in the retrievals. Due to the lack of the laboratory or *in-situ* measurements, it is common and especially important for exoplanet atmosphere applications to use interpolations to obtain continuous data (see for example, Kitzmann & Heng 2017).

**The Granada-Amsterdam light scattering data base**<fnref n="50" /> (Muñoz et al. 2012) contains experimental scattering matrices as functions of the scattering angle of samples of small irregular particles. These are laboratory experiments typically performed at two optical wavelengths. Laboratory data such as these are important to be able to benchmark theoretical codes, particularly those which model the scattering matrices of irregularly shaped particles. Available codes for computing complex dust particle opacities, both for spherical and fractal shapes, include optool (Dominik, Min & Tazaki 2021),<fnref n="51" /> the discrete-dipole-approximation code ADDA (Yurkin & Hoekstra 2011),<fnref n="52" /> the T-matrix codes (Mackowski & Mishchenko 1996)<fnref n="53" /> and CORAL (Lodge, Wakeford & Leinhardt 2023).<fnref n="54" /> Other well-used codes include PyMieScatt<fnref n="55" /> (Sumlin, Heinson & Chakrabarty 2018), which can deal with both direct and inverse Mie calculations.

### 6.1.2 Thermochemical data

Thermochemical data are required as input to kinetic cloud models of exoplanet atmospheres, for example in the computation of bulk growth processes for a particular species as a function of pressure

and temperature, which requires gas-surface reactions (Helling 2021). There are several thermochemical properties of interest for astrophysical studies, including the Gibbs free energies of formation, the change in enthalpy, or the entropy (Lecoq-Molinos et al. 2024). Chemical and cluster data for all the species that take part in the cloud formation process are required for complete atmospheric models. Particularly, cluster data are crucial to model the nucleation process and the formation of cloud condensation nuclei (CCN). Data bases, including the NIST-JANAF Thermochemical tables (Chase 1998)<fnref n="56" /> and the other data bases listed in Section 7.1.1 contain thermochemical data but rarely cluster data. As noted by Helling (2021), laboratory data are often not available, and so thermochemical data for various species can also be computed. For example, thermochemical data for TiO₂ nanoclusters have been recently computed by Sindel et al. (2022), and for VₓOᵧ clusters by Lecoq-Molinos et al. (2024). These data are appropriate for use in modelling the formation of cloud condensation nuclei in exoplanet atmospheres. A variety of other sources for thermochemical data exist. For example, for TiO₂ data are provided by Jeong et al. (2000), Lee et al. (2015), and Sindel et al. (2022), with Jeong et al. (2000) also providing information about other intermediate TiₓOᵧ structures, including their vibrational frequencies. For SiO and SiO₂ thermochemical data are available from works such as Bromley, Gómez Martín & Plane (2016) and Flint & Fortenberry (2023). For MgO, Köhler, Gail & Sedlmayr (1997) provide thermochemical data, and Chen et al. (2014) provide cluster structures and vibrational spectra for (MgO)ₓ (but not thermochemical data directly). Other species with thermochemical data include silicon carbide (Gobrecht et al. 2017), KCl (Rose & Berry 1992), silicates (Goumans & Bromley 2012), magnesium and calcium aluminates (Gobrecht et al. 2023), aluminium oxides (Gobrecht et al. 2022, 2018; Lam et al. 2015; Patzer et al. 2005), and Fe-bearing species (Chang et al. 2013; Ding et al. 2009).

Condensation curves (or vapour pressure curves), which indicate the thermal stability of a species, are necessary inputs into codes which predict the expected cloud materials present in an exoplanet's atmosphere based on its pressure and temperature structure, typically found via global climate models (GCMs). Condensation curves appropriate for modelling exoplanet atmospheres up to high temperatures are available from a number of sources, with those for some typical species thought to be important in exoplanet atmospheres given in Table 13. Gao et al. (2020a)<fnref n="57" /> give expressions for condensation curves from a number of different sources, including some of those mentioned in Table 13.

For cooler atmospheres, such as those in our solar system, ices are formed when gaseous species become supersaturated. The altitudes where the saturation occurs is a function of the mixing ratio of the species in question and the atmospheric temperature. Species dependent condensation curves will therefore vary with season and latitude, for example in the case of benzene in Titan, the latitudinal dependence is particularly evident in the fall at the poles where the decrease in stratospheric temperature is more dramatic and leads to an increase in the mole fractions. Barth (2017) extrapolated high-temperature vapour pressure data of benzene (C₆H₆) to lower temperatures to calculate benzene condensation curves. Their results predicted benzene and HCN to condense at near identical altitudes for the temperature profiles. However, Dubois et al. (2021) measured the equilibrium vapour pressure of pure crystalline benzene (C₆H₆)

<footnote n="48">⁴⁸https://github.com/exoclime/LX-MIE</footnote>

<footnote n="49">⁴⁹https://stellarplanet.org/science/condensates/</footnote>

<footnote n="50">⁵⁰https://www.iaa.csic.es/scattering/list/index.html</footnote>

<footnote n="51">⁵¹https://github.com/cdominik/optool.git</footnote>

<footnote n="52">⁵²https://github.com/adda-team/adda.git</footnote>

<footnote n="53">⁵³https://www.giss.nasa.gov/staff/mmishchenko/tmatrix/</footnote>

<footnote n="54">⁵⁴https://github.com/mglodge/CORAL.git</footnote>

<footnote n="55">⁵⁵https://pymiescatt.readthedocs.io/en/latest/</footnote>

<footnote n="56">⁵⁶https://janaf.nist.gov/</footnote>

<footnote n="57">⁵⁷https://github.com/natashabatalha/virga/blob/master/virga/pvaps.py</footnote>
thermochemical data as input. Many codes make use of the algorithm of Gordon & Mcbride (1994) to compute the free energy minimisation.

Here we give a little more context to what data are required for codes such as these. Each species is characterised by two sets of thermochemical coefficients, that can be in a 7-format or in a 9-format. The 9-format, being a more recent one, permits more accurate calculations. There is a set for the “high-temperature” range and another one for the “low-temperature” range. For each of these ranges, thermochemical properties of each species in the Normal Conditions Enthalpy ($h^0$) and Entropy ($s^0$) can be calculated thanks to the NASA polynomials. For the 7-format, the expressions are:

$$ \frac{h^0(T)}{RT} = a_1 + \frac{a_2 T}{2} + \frac{a_3 T^2}{3} + \frac{a_4 T^3}{4} + \frac{a_5 T^4}{5} + \frac{a_6}{T} \quad (7) $$

$$ \frac{s^0(T)}{R} = a_1 \ln T + a_2 T + \frac{a_3 T^2}{2} + \frac{a_4 T^3}{3} + \frac{a_5 T^4}{4} + a_7. \quad (8) $$

The polynomials corresponding to the 9-format are slightly different:

$$ \frac{h^0(T)}{RT} = -a_1 T^{-2} + a_2 T^{-1} \ln T + a_3 + \frac{a_4 T}{2} + \frac{a_5 T^2}{3} + \frac{a_6 T^3}{4} + \frac{a_7 T^4}{5} + \frac{b_1}{T} \quad (9) $$

$$ \frac{s^0(T)}{R} = -\frac{a_1 T^{-2}}{2} - a_2 T^{-1} + a_3 \ln T + a_4 T + \frac{a_5 T^2}{2} + \frac{a_6 T^3}{3} + \frac{a_7 T^4}{4} + b_2. \quad (10) $$

With these two quantities calculated, the Gibbs Free Energy of the system can be calculated:

$$ G_{sys} = \sum_{l}^{L} (h_{l}^{0}(T) - T s_{l}^{0}(T) + RT \ln \frac{P}{P^{0}} + RT \ln N_{l}) \times N_{l} \quad (11) $$

The sets of $N_l$ that will minimise this equation (the Gibbs Free Energy of the system) will correspond to the composition at thermochemical equilibrium. Some databases which gather equilibrium thermodynamic coefficients for large sets of species are listed in Section 7.1.1.

### 7.0.2 Disequilibrium chemistry

In addition to equilibrium chemistry, some codes include disequilibrium chemistry processes (i.e. quenching induced by vertical mixing, photodissociation, condensation and evaporation, surface deposition, outgassing and atmospheric escape) that control the atmospheric chemical composition (i.e. García Muñoz 2007; Moses et al. 2011; Venot et al. 2012, 2020; Tremblin et al. 2016; Rimmer & Helling 2016; Tsai et al. 2017; Al-Refaie et al. 2022). These codes require data adapted to the type of exoplanets studied. For instance, for warm exoplanets, such as hot Jupiters, data which are validated/measured/calculated at high temperatures are necessary. The data are: chemical schemes, that is to say a list of hundreds to thousands of reactions, with the associated reaction rates, but also physico-chemical data for photodissociations (UV absorption cross-sections and quantum yields). Thermodynamic data are also necessary to calculate reverse reactions rates.

## 7.1 State of the art - Data availability

### 7.1.1 Equilibrium chemistry

The C/H/O/N (CHON) chemical species included in ACE (Agúndez et al. 2012, 2020) are mainly sourced from Gordon & Mcbride (1994), Atkinson et al. (2004, 2006, 2007) and Bounaceur et al. (2010), with some constants computed using THERGAS (Muller et al. 1995). ACE is commonly used in conjunction with chemical kinetic models by Venot et al. (2012, 2020) and in the more recently re-written Python version FRECKLL (Al-Refaie et al. 2022). FastChem (Stock et al. 2018, 2022) sources their thermochemical data from the NIST-JANAF database (Chase 1998)<fnref n="61" />. A version of FastChem has recently been released which includes equilibrium condensation and rainout (Kitzmann et al. 2023), and comes with a python interface. GGchem (Woitke et al. 2018) uses multiple sources of thermochemical data, with new species continuously added to the data files on the GitHub page. The sources for their chemical data comes from (Barklem & Collet 2016), NIST-JANAF (Chase 1998) and Tsuji (1973). Appendix C of Woitke et al. (2018) includes a comparison of the molecular equilibrium constants collected from various sources; the full comparison data can be found at Worters et al. (2017). A graphical comparison of mineral Gibbs free Energy data can also be found at Woitke et al. (2017). Other sources of thermochemical data include the Burcat database (Burcat et al. 2005)<fnref n="62" /> and Sharp & Huebner (1990).

### 7.1.2 Disequilibrium chemistry

Concerning chemical schemes, Venot et al. (2012, 2015, 2020) and Veillet et al. (2024) have developed and updated reliable chemical schemes validated through combustion experiments for CHON species. Chemical schemes with sulphur have also been developed and presented in various studies, such as Zahnle et al. (2016), Tsai et al. (2021), and Hobbs et al. (2021). The network of Tsai et al. (2021) was recently used to contextualise the photochemically produced SO₂ feature detection in the atmosphere of hot Jupiter WASP-39 b (Tsai et al. 2023). Other disequilibrium chemical schemes tailored for exoplanets include the STAND network (Rimmer & Helling 2016), which includes effects of UV photochemistry, cosmic ray chemistry and lightning-driven chemistry, and the network of Moses et al. (2010) which uses thermochemical/photochemical kinetics and transport models. ExoREL (Hu 2019, 2021) is a code to compute disk-averaged reflected-light spectra at any planetary phase, taking into account the radiative properties of clouds and their feedback on planetary albedo.

Databases such as the JPL Chemical Kinetics and Photochemical Data database<fnref n="63" />, the Kinetic Database for Astrochemistry (KIDA) database<fnref n="64" /> (Wakelam et al. 2012), the NIST chemical kinetic database<fnref n="65" />, and the UMIST Database for Astrochemistry (UDfA)<fnref n="66" /> are all sources of kinetic and photochemical data. KIDA provides evaluations of reaction

<footnote n="61">⁶¹ https://janaf.nist.gov/janbanr.html</footnote>

<footnote n="62">⁶² http://garfield.chem.elte.hu/Burcat/burcat.html</footnote>

<footnote n="63">⁶³ https://jpldataeval.jpl.nasa.gov/index.html</footnote>

<footnote n="64">⁶⁴ https://kida.astrochem-tools.org/</footnote>

<footnote n="65">⁶⁵ https://kinetics.nist.gov/kinetics/</footnote>

<footnote n="66">⁶⁶ http://udfa.ajmarkwick.net</footnote>
<table><caption>**Table 13.** Example of sources used for vapour pressure/condensation curves of various materials typically thought to be important in exoplanet atmospheres, particularly those at higher temperatures such as hot Jupiters.</caption><thead><tr><th>Material</th><th>Reference</th><th>Note</th></tr></thead><tbody><tr><td>Al<sub>2</sub>O<sub>3</sub></td><td>Wakeford et al. (2016)</td><td>Following the approach of Visscher, Lodders & Fegley (2010)</td></tr><tr><td>CaTiO<sub>3</sub></td><td>Wakeford et al. (2016)</td><td>—</td></tr><tr><td>KCl</td><td>Morley et al. (2012)</td><td>—</td></tr><tr><td>Fe</td><td>Visscher et al. (2010)</td><td>—</td></tr><tr><td>Fe<sub>2</sub>O<sub>3</sub></td><td>Sharp & Huebner (1990)</td><td>Coefficients for a large number of condensed species are given in Table 2(b)</td></tr><tr><td>Fe<sub>2</sub>SiO<sub>4</sub></td><td>Sharp & Huebner (1990)</td><td>—</td></tr><tr><td>FeO</td><td>Sharp & Huebner (1990)</td><td>—</td></tr><tr><td>FeS</td><td>Woitke et al. (2018)</td><td>Equation inside GGchem source code</td></tr><tr><td>MgAl<sub>2</sub>O<sub>4</sub></td><td>Woitke et al. (2018)</td><td>Equation inside GGchem source code</td></tr><tr><td>Mg<sub>2</sub>SiO<sub>4</sub></td><td>Visscher et al. (2010)</td><td>—</td></tr><tr><td>MgSiO<sub>3</sub></td><td>Visscher et al. (2010)</td><td>—</td></tr><tr><td>SiO<sub>2</sub></td><td>Grant et al. (2023)</td><td>—</td></tr><tr><td>SiO</td><td>Gail et al. (2013)</td><td>Older data from Nuth-III & Ferguson (2006), Schick (1960)</td></tr><tr><td>TiO<sub>2</sub></td><td>Woitke & Helling (2004)</td><td>Powell et al. (2018) give this formula rewritten in different units</td></tr></tbody></table>

at temperatures relevant to Titan. Their measurements indicated that the prior extrapolations from literature values underestimated the vapour pressure. Feeding their laboratory data into a microphysical model, Dubois et al. (2021) showed that $C_6H_6$ polar cloud particles form first at higher altitudes and continue to exist at all latitudes down to the saturation point of HCN at slightly lower altitudes where condensation of $C_6H_6$ and HCN is possible. This is because the larger measured vapour pressure of $C_6H_6$ allows for larger ice particles that continue to grow deeper down in the troposphere, beyond the condensation point of HCN. It is therefore important for accurate condensation curve data to be available across a wide range of temperatures for a large number of species relevant to (exo)planetary and solar system atmospheres.

Notable sources for condensation curve formulae which can be used in atmospheric models include Visscher, Lodders & Bruce Fegley (2006), who give approximations for condensation curves of various sulphur- and phosphorus-bearing species, and Visscher et al. (2010) for iron- silicon- and magnesium-bearing species. Morley et al. (2012) compute condensation curves for Cr, MnS, $Na_2S$, ZnS, and KCl, for use in characterizing brown dwarf atmospheres, based on the thermochemical models of Visscher et al. (2006, 2010). Wakeford et al. (2016) give expressions for equilibrium condensation temperatures as functions of pressure and metallicity for various Al- and Ti-bearing species. Appendix A of Ackerman & Marley (2001) provides relations for saturation vapour pressures of a few species, such as $NH_3$, $H_2O$, Fe, and $MgSiO_3$ (enstatite). For $NH_3$ they fit measurements tabulated in the CRC Handbook of Chemistry and Physics (Weast 1971). For $H_2O$ they make use of data from Buck (1981), and from Barshay & Lewis (1976) for Fe and enstatite. Section 3.2 of Woitke et al. (2018), along with their supplementary document (Woitke, Helling & Turner 2017), give a detailed comparison of condensed phase data from either the NIST-JANAF database (Chase 1998) or the geophysical SUPCRTBL data base<fnref n="58" /> (Johnson, Oelkers & Helgeson 1992; Zimmer et al. 2016). They offer comparisons of 121 species, and find good agreement for all species apart from anorthite, $CaAl_2Si_2O_8$. These data are used in Woitke et al. (2018) to compute vapour pressure curves, with an example for Fe shown in their fig. D.1.

Fig. 4 of Gail et al. (2013) illustrates the importance of using the most up-to-date measurements for vapour pressure curves, as

they compare their experimentally determined data for SiO with data from Nuth-III & Ferguson (2006) and Schick (1960). There is a considerable difference between the curves shown for SiO, with the newer measured data allowing SiO to be present in solid form up to higher temperatures than the older data. It is recommended that any users of thermochemical data such as condensation curves cite the sources of their data to allow for comparisons and for re-analysis in the case of newer data being produced. A thorough investigation into the reliability of the data used for condensation curves of species important for characterizing (exo)planetary atmospheres such as those mentioned here, including additional newer measurements, would be a worthwhile endeavour, useful for assessing which species are expected in the atmospheres of current and future observations.

### 6.1.3 Measured cloud and haze production in the laboratory

Atmospheric chamber experiments can be used to simulate the atmospheres of Solar System bodies, in order to study haze formation, and measure transmittance and reflectance spectra. This has been done for Titan (Imanaka et al. 2004; He et al. 2022a), Triton (Moran et al. 2022), and early Earth (DeWitt et al. 2009; Hörst et al. 2018b); in such studies many haze properties can be measured, such as production rate, bulk composition, molecular composition, and their transmission and reflectance from the optical to the near-infrared. The latter is done with Fourier transform infrared (FTIR) spectroscopy. In the case of He et al. (2022a), they provide a set of optical constants of Titan haze analogues in the wavelength range 0.4–3.5 µm. Similar atmospheric chamber experiments have been used to study the formation of water ice clouds on Mars (Phebus et al. 2011), organic aerosols in anoxic and oxic atmospheres of Earth-like exoplanets (Gavilan et al. 2018), precursors to haze formation in cool exoplanet atmospheres (He et al. 2019), and to explore haze production in super-Earth and mini-Neptune atmospheres (Hörst et al. 2018a).

Several groups contribute to data bases of refractive indices using different laboratory setups, such as COSMIC at NASA Ames (Sciamma-O’Brien et al. 2023), PAMPRE at LATMOS (Gavilan et al. 2018; Drant et al. 2024), and John Hopkins University (He et al. 2022a). Previous work on Titan haze analogues revealed that the setup conditions likely affect the analogue’s composition and thus their refractive indices (see e.g. Brassé et al. 2015). Further laboratory experiments are important to fully understand the composition of different solid particles and their resulting optical properties.

<footnote n="58">⁵⁸https://models.earth.indiana.edu/supcrtbl.php</footnote>
rates and uncertainties, and contains chemical schemes used in planetology. Concerning UV photochemical data, very few data exist at high temperatures. Venot et al. (2013, 2018) studied experimentally the thermal dependency of carbon dioxide and its impact on atmospheric composition. Grosch et al. (2015) measured UV absorption cross-sections of sulphur-containing compounds at elevated temperatures. See Section 8 for details on UV photodissociation and photoabsorption cross-sections.

## 7.2 Data used by retrieval codes

### 7.2.1 Equilibrium chemistry

Many of the aforementioned equilibrium chemistry codes are included in various atmospheric modelling and retrieval codes, such as TauREx3 (Al-Refaie et al. 2021) where ACE is included as standard with the option to include others using the plugin feature of TauREx 3.1, and ARCIS (Ormel & Min 2019; Min et al. 2020), where GGchem (Woitke et al. 2018) is fully integrated. FastChem (Stock et al. 2018, 2022) is the main chemical model used in the HELIOS radiative transfer code (Malik et al. 2017; Kitzmann et al. 2020). Other codes such as petitRADTRANS (Mollière et al. 2019) and Exo-REM (Baudino et al. 2015) include self-written chemical routines. For example, easyCHEM is a self-written Gibbs free energy minimizer which is included in petitRADTRANS and described in Mollière et al. (2017). Al-Refaie et al. (2022) compares computed atmospheric spectra of a hot gas giant exoplanet using some of the available equilibrium chemistry codes plugged into TauREx3; ACE (Agúndez et al. 2012, 2020), FastChem (Stock et al. 2018), GGchem (Woitke et al. 2018), and also GGchem with condensation included. Jaziri et al. (2024) explore the reliability of 1D equilibrium chemistry models to predict the composition of 3D exo-atmospheres with simulated transmission spectra of Ariel and JWST observations. They use the ACE chemical scheme in the TauREx retrieval code. ATMO (Tremblin et al. 2015; Tremblin et al. 2016) is a radiative/convective equilibrium code which is coupled to the CHNO-based chemical network of Venot et al. (2012).

### 7.2.2 Disequilibrium chemistry

In addition to equilibrium chemistry, some atmospheric retrieval codes explicitly parameterise the disequilibrium chemistry process of quenching. For example, Kawashima & Min (2021) outlines the disequilibrium chemistry scheme included in atmospheric retrieval and modelling code ARCIS (Ormel & Min 2019; Min et al. 2020), which includes vertical mixing of CH₄, CO, H₂O, NH₃, N₂, and CO₂. ARCIS uses thermochemical data collated as part of GGchem (mentioned above; Woitke et al. 2018), along with some extra species sourced from Burcat et al. (2005) and Ruscic et al. (2005). Recently, a step forward has been made with TauREx3 using the kinetic code FRECKLL (Full and Reduced Exoplanet Chemical Kinetics distiLLed) as a plugin (Al-Refaie et al. 2022), in order to perform retrievals taking into account chemical kinetics. The kinetic network included in FRECKLL is based on the chemical scheme of Venot et al. (2020). VULCAN is another open source chemical kinetics code for exoplanetary atmospheres, written in python (Tsai et al. 2017). Their paper describes the rate coefficients and thermodynamic data used in the network. ATMO (Tremblin

et al. 2015; Tremblin et al. 2016) includes a kinetic chemical network, as well as the equilibrium chemistry network described above. HyDRA (Gandhi & Madhusudhan 2017) is a disequilibrium retrieval framework for thermal emission spectra of exoplanetary atmospheres.

## 7.3 What's being worked on?

A chemical scheme validated through combustion experiment coupling CHON species to sulphur species is currently in progress and should be published in 2024 (Veillet et al., in prep). There is currently work underway to include P and S in CHON chemical schemes, using ab initio methods. Sticking coefficients of gas phase molecules onto solid atmospheric haze are currently in progress and should be published in 2024 (Perrin et al. in prep).

## 7.4 Data needs: What's missing and urgent?

(i) **Rocky exoplanets:** data for rocky exoplanets require chemical networks that include many different species, which can be much more complex than what is usually the case for hydrogen-dominated atmospheres. These species include KOH, NaOH, SiO₂, CaOH, FeO, MgO, MgOH, as outlined in works such as Tennyson & Yurchenko (2017) and Herbert et al. (2020).

(ii) **Reaction rates for various different molecules:** Helling (2021) call for more thermochemical data, particularly reaction rates, for elements other than CHON(S). This has been pointed out by others, for example for phosphine (Bains et al. 2021), which is applicable to Venus as well as exoplanet atmospheres; for chlorine-bearing species which would be helpful for Mars; and species such as TiO and VO for high-temperature exoplanet atmospheres. Other elements which ideally would be included in chemical schemes include Si, Mg, Na, K, Ca, and Al (Fortney et al. 2019).

(iii) **Reaction rates for ions:** ion chemistry may affect the deeper layers of the atmosphere through, for example, the formation of haze. The ionosphere is also relevant because it connects with the upper atmospheres and therefore with the long-term evolution of the planet's composition.

(iv) **Reaction rate coefficients at a variety of pressures and temperatures:** Reaction rates must therefore be known at temperatures ranging from ~30 K to above 3000 K, and because the deep atmospheric layers are chemically mixed with the layers probed by spectroscopic observations, at pressures up to about ~100 bar.

(v) **Condensation:** some codes, such as GGchem (Woitke et al. 2018) and the latest version of FastChem (Kitzmann et al. 2023), already include condensation. Expanding more codes to explicitly include condensation and including additional elements in chemical networks will be useful to fully characterise where the elements of an atmosphere will go under chemical equilibrium conditions.

(vi) **Gas-particle processes:** when aerosol particles are present in the atmosphere, gas-particle chemistry has been shown to influence not only the particles, but also the atmospheric content (Cavalié et al. 2023; Petera et al. 2023, and Knížek et al. in prep.). For that purpose, sticking coefficients of gas components on solid particles are largely missing.
### 6.1.4 Surface reflectance

For terrestrial exoplanets and exomoons, the bottom boundary layer of the atmosphere must be considered as the surface. Table 14 summarizes the most important data bases of surface reflectance in Planetary Science. They encompass Earth natural rocks analogues, but also synthetic materials in the lab (such as ices), meteorites and *in-situ* samples measured back in Earth, such as the Moon or Ryugu asteroid. The reflectance spectrum of Phobos was measured from 0.4 to 4.75 µm by Wargnier et al. (2023). The data set from *in-situ* measurements of reflectance in planetary science is usually scattered and unfortunately, such a data base is missing.

Reflectance measurements can be done over several wavelength ranges, but also for different observation geometries (incidence and emergence angles), leading to BiDirectionnal Reflectance Distribution Function (BRDF). Laboratory measurements can also be done for various surface properties (such as grain size, grain shape, compaction, and roughness) but also various kinds of mixtures (such as linear geographic mixture, intimate mixture, and intragrain mixture).

The data bases which contain information on observation geometries and the nature of the samples do not always contain optical constants, as can be seen by Table 14. Additionally some data bases contain characterization of the samples using other methods of investigations (Raman spectroscopy, petrological description, X-ray diffraction, Laser Induced Breakdown Spectroscopy, X-ray fluorescence, or electron microprobe analysis). The SSHADE (Solid Spectroscopy Hosting Architecture of Data bases and Expertise) (Schmitt et al. 2018) data base regroups several data bases. It provides spectral and photometric data obtained by various spectroscopic techniques including reflectance spectroscopy. The measured samples include ices, pure minerals, rocks, organic, and carbonaceous materials as well as liquids. They are either synthetic, natural samples from Earth or other planetary bodies such as (micro-) meteorites or lunar soils. Additional characterizations of the samples are not systematically done but optical constants are often available and observation geometries are generally given. The USGS-Speclab (Kokaly et al. 2017) data base regroups spectra measured with laboratory, field, and airborne spectrometers, acquired on samples of specific minerals, plants, chemical compounds, and man-made materials. It also contains mathematically computed mixtures. The RELAB data base regroups spectroscopic data acquired on pure minerals, natural, and synthetic samples. The ECOSTRESS data base (Meerdink et al. 2019) regroups data acquired on natural and man-made materials. The Mineral and Rock Sample Data base University of Winnipeg (Cloutis 2015) contains data acquired on pure minerals and natural or synthetic mixtures. Finally, the PTAL (Planetary Terrestrial Analogue Library; Dypvik et al. 2021) regroups data acquired on whole rocks. Additional characterization are systematically done by multiple techniques.

## 6.2 Data used by retrieval codes

There are a number of exoplanetary atmospheric modelling and retrieval codes which include the effects of solid-state aerosol species in their radiative transfer calculations, and thus require wavelength-dependent, and ideally also temperature-dependent, refractive index data (e.g. Wakeford & Sing 2015; Pinhas & Madhusudhan 2017; Min et al. 2020; Ma et al. 2023; Taylor & Parmentier 2023). The LRS instrument onboard JWST, covering ~ 5–12.5 µm, allows for observations of vibrational-mode aerosol signatures which are commonly found in this wavelength region; for example quartz

clouds were inferred in hot Jupiter WASP-17 b by Grant et al. (2023) (using optical constants of crystalline SiO₂ at 928 K from Zeidler et al. 2013) and silicate clouds (MgSiO₃, SiO₂, and SiO) have been inferred in hot Neptune WASP-107 b by Dyrek et al. (2024).

Many retrieval codes have their own built-in library of refractive indices, the option to directly include pre-computed cloud opacities, or both. See for example table 1 of Min et al. (2020) for species included in atmospheric modelling and retrieval code ARCIS, with the option to read in your own refractive index data and use the code to compute either spherical or irregularly shaped particles (Min, Hovenier & de Koter 2005) of a defined particle size distribution. petitRADTRANS (Mollière et al. 2019) also has a list of built-in cloud opacities,<fnref n="59" /> with the option to add more. There are also available add-ons which can be coupled with existing retrieval codes. For example, *YunMa* is an exoplanet cloud simulation and retrieval package (Ma et al. 2023) which can be coupled to the TauREx3 platform to retrieve microphysical cloud properties in exoplanet atmospheres.

Typically in cases of data bases and compilations, the original citation for the measured or computed refractive indices is mentioned. It is important these citations are also included in any atmospheric modelling or retrieval works, as there may be future updates for particular species. Works such as Zeidler et al. (2015) highlight the importance of this; they show temperature-dependence has an impact on the refractive indices and therefore the spectral features which are included in atmospheric models for enstatite and olivine.

The refractive indices determined by using different techniques can differ from one another; for example in the case of amorphous MgSiO₃ (see Table 12) Jäger et al. (2003) use the sol-gel method, and Dorschner et al. (1995) use melting and quenching technique; a comparison between the two is made in fig. 4 of Jäger et al. (2003).

Surfaces are often neglected but some retrievals propose to include surface reflectance measurements by linear mixing (Cui et al. 2018). More advanced radiative transfer modelling will be used, based on optical constants, for instance using the Hapke model (Hapke 1993). Surface reflection is also an important component of studies such as Stam (2008), Fauchez, Rossi & Stam (2017), and Trees & Stam (2022), who consider the reflection spectra of a planet's atmosphere and surface (if there is one), including polarization state; these studies are particularly useful for modelling the observations of future instruments such as the Habitable Worlds Observatory (HWO) (Vaughan et al. 2023).

## 6.3 What's being worked on?

Ongoing laboratory experiments (see for example, setups described in works such as Deguine et al. 2020; Drant et al. 2024; He et al. 2024) are essential for measuring new refractive indices, exploring photochemical pathways, and verifying theoretical calculations related to aerosols in planetary and exoplanet atmospheres. He et al. (2024), for example, recently measured the density and optical properties of organic haze analogues generated in water-rich exoplanet atmosphere experiments, for use in characterizing observations from JWST. Gao et al. (2021), who give an overview of the state of knowledge (as of 2021 February) of aerosols for exoplanet atmospheres, highlight the importance of ongoing laboratory experiments and development of theoretical methods. A recent review by Sciamma-O'Brien and

<footnote n="59">⁵⁹https://petitradtrans.readthedocs.io/en/latest/content/available_opacities.html</footnote>
## 8 UV PHOTODISSOCIATION/PHOTOABSORPTION DATA

The need for a description of molecular absorption in the ultraviolet (UV) region is motivated mainly by the following two reasons: 1) direct observations in the UV, which are not in the Ariel instrumental range but are of relevance to a number of existing, past, or potential future instruments; the WFC3/UVIS instrument on board the Hubble Space Telescope (HST) (200 - 1000 µm), the Galaxy Evolution Explorer (GALEX) (135 - 280 nm) (Viswanath et al. 2020), the Far Ultraviolet Spectroscopic Explorer (FUSE) (90.5 - 119.5 nm) (Moos et al. 2004), Cassini Ultra-violet Imaging Spectrograph (UVIS) (55.8-190 nm) (Esposito et al. 2004), and the Habitable Worlds Observatory (HWO) future mission concept; and 2) utilization in atmospheric chemistry calculations for considering the chemical consequences of photodissociation, see Section 7.

The special but typical feature of molecular spectra in the UV/VUV wavelength region is their continuum. Their featureless character is due to the dissociative effects in the high energy/low wavelength region. It is this type of spectra produced by transitions to or from states above a dissociation limit and caused by the dissociation that we mainly discuss in this section. Photoabsorption includes both bound-bound transitions (these appear as either sharp lines or continuum) and bound-free photodissociation (continuum) transitions, see, e.g., Pezzella et al. (2021); Tennyson et al. (2023). Bound-bound transitions which result in sharp lines are often included as part of a line list which extends into the UV region (see Section 2). These types of spectra can be completely featureless for transitions to the continuum (from bound states to fully unbound, or dissociative states, e.g. HF above 100-160 nm (Pezzella et al. 2022)) or mostly consisting of sharp lines (from bound to bound or quasi-bound states, e.g. MgH in the A-X band at 0.5 µm (Owens et al. 2022a)), or anything in between (Pezzella et al. 2022). Both continuum absorption and photodissociation need to be represented as cross-sections rather than the lines of a typical line list, as they are continuum processes.

This creates challenges not only for the production of UV spectral data but also for its provision and format. For the featureless UV spectra, it is natural to provide data in the form of temperature dependent cross-sections. This is the format of the MPI-Mainz UV/VIS Spectral Atlas<sup>67</sup> (Keller-Rudek et al. 2013) and Leiden<sup>68</sup> (Heays et al. 2017; Hrodmarsson & Van Dishoeck 2023) databases. In cases where the cross-sections are dominated by the continuum, the pressure effect is considered to be negligible, but it is noteworthy that in cross-sections dominated by resonances to Rydberg and valence states, high pressures can significantly affect the absorption cross-sections. Such effects have been observed in NO where transitions to Rydberg states were significantly broadened and shifted up until 1000 bar where the Rydberg states have all but vanished from the spectrum (Miladi et al. 1975, 1978). However, most UV spectra comprise a mixture of the bound-bound transitions (sharper lines) and continuum.

## 8.1 State of the art - Data availability

One of the main sources of photodissociation cross-sections of molecules is the Leiden database<sup>69</sup> which provides photodissociation and photoionisation cross-sections for 116 atoms and molecules of astrochemical interest collected from the literature, both experimental and theoretical (Heays et al. 2017; Hrodmarsson & Van Dishoeck 2023). The photodissociation thresholds and threshold products provided by this database are summarised in Table 13, and the provided photoionisation thresholds summarised in Table 14. The PHIDRATES website<sup>70</sup> provides a set of tools to calculate photoionization, photodissociation, and photodissociative ionization rate coefficients (Huebner et al. 1992). There is also the MPI-Mainz UV/VIS Spectral Atlas<sup>71</sup> (Keller-Rudek et al. 2013), where experimental temperature dependent cross-sections for a larger number of molecules are provided. We note, however, that there are some limitations in terms of temperature-dependence and wavelength coverage of the data in cross-section databases such as these, so it is worth checking the range of temperatures covered by the cross-sections for each species of interest.

In the past few years, there have been multiple VUV cross-sections (photoabsorption and photoionization) measured at the VUV DESIRS beamline at the SOLEIL synchrotron facility in France (Nahon et al. 2012). Absolute photoabsorption cross-sections have been measured using a Fourier-transform absorption cell (de Oliveira et al. 2016) where multiple species have been measured such as OH (Heays et al. 2018), CO (Hakalla et al. 2016; Lemaire et al. 2018), S<sub>2</sub> (Stark et al. 2018), SO (Heays et al. 2023), and others. Absolute photoionization cross-sections of radical species have been measured with the double-imaging photoelectron photoion (i2PEPICO) instrument DELICIOUS3 (Garcia et al. 2013) where the radicals were produced continuously in a stable manner with a flow tube (Garcia et al. 2015). This has allowed the measurements of photoionization cross sections of species such as OH (Harper et al. 2019), NH<sub>2</sub> (Harper et al. 2021), SH (Hrodmarsson et al. 2019), and others. In the frame of the EXACT project<sup>72</sup> VUV absorption cross-sections are also measured experimentally. The first species studied was carbon dioxide (Venot et al. 2013, 2018).

Regarding computations of photoionisation or photoabsorption, the XCHEM code has recently been used to compute the photoionisation of H<sub>2</sub>O (Fernández-Milán et al. 2023), and also the valence-shell photoionisation of CO<sub>2</sub> (Pranjal et al. 2024). Photoabsorption cross-sections for atmospheric volatile organic compounds (VOCs) have been computed by Prlj et al. (2020, 2022), using a combination of time-dependent and time-independent methods. Ndengué et al. (2023) explore the temperature dependence of the electronic absorption spectrum of NO<sub>2</sub> up to 2200 K, down to a wavelength of 0.25 µm (up to 40,000 cm<sup>-1</sup>) by combining *ab initio* methods with high-resolution experiments. Xue et al. (2020) utilise machine learning methods for the computation of absorption cross-sections in the visible and UV wavelength region, including benzene. Machine learning algorithms for the

<footnote n="67">67 https://www.uv-vis-spectral-atlas-mainz.org/uvvis/</footnote>

<footnote n="68">68 https://home.strw.leidenuniv.nl/~ewine/photo/index.html</footnote>

<footnote n="69">69 https://home.strw.leidenuniv.nl/~ewine/photo/index.html</footnote>

<footnote n="70">70 https://phidrates.space.swri.edu/</footnote>

<footnote n="71">71 https://www.uv-vis-spectral-atlas-mainz.org/uvvis/</footnote>

<footnote n="72">72 https://www.anr-exact.cnrs.fr/</footnote>
<table><caption>**Table 14.** Sources for the reflectance data of various materials typically used in (exo)planetary surface models.</caption><thead><tr><th>Name</th><th>Reflectance</th><th>Optical constants</th><th>Link</th></tr></thead><tbody><tr><td>SSHADE</td><td>X</td><td>X</td><td>https://www.sshade.eu/</td></tr><tr><td>USGS-Speclab</td><td>X</td><td>-</td><td>https://crustal.usgs.gov/speclab/</td></tr><tr><td>RELAB</td><td>X</td><td>-</td><td>https://sites.brown.edu/relab/relab-spectral-database/</td></tr><tr><td>ECOSTRESS</td><td>X</td><td>-</td><td>https://speclib.jpl.nasa.gov</td></tr><tr><td>Mineral and rock sample</td><td>X</td><td>-</td><td>https://www.uwinnipeg.ca/c-tape/sample-database.html</td></tr><tr><td>Database University of Winnipeg</td><td>-</td><td>-</td><td></td></tr><tr><td>PTAL</td><td>X</td><td>-</td><td>http://erica.uva.es/PTAL/</td></tr></tbody></table>

Drant & Wogan (2024) further highlights the importance of measured optical constants of laboratory aerosols for interpreting *JWST* observations.

As mentioned in Section 6.1.3, there are several groups with different laboratory setups who are working on measuring the optical properties of different aerosol analogues. Collaborations are currently ongoing between LATMOS (Gavilan et al. 2018; Drant et al. 2024) and NASA Ames (Sciamma-O'Brien et al. 2023) to compare optical properties found with different laboratory setups. Such collaborations will provide more complete data sets and help better understand implications for future observations. Work is ongoing to combine different laboratory spectroscopic measurements in order to provide continuous data of refractive indices across a broad spectral range covering the ranges of *JWST* and *Ariel*; see for example, He et al. (2024) and Drant et al. (2024) for current examples of this.

For condensation curve data, work is ongoing from groups such as those at NASA Ames, where three groups are working independently to address the different properties and optical constants required for accurate condensation curves.

## 6.4 Data needs: what's missing and urgent?

**(i) Wavelength coverage:** as shown in Table 12, the available data for a number of aerosols do not have the wavelength-coverage required to cover the observational range of *Ariel* (0.5–7.8 µm) and/or other telescopes related to exoplanet atmospheres such as *JWST* (~0.6–28.5 µm, with the LRS instrument covering ~5–13 µm). We also note that refractive indices are important for modelling reflectance spectra of exoplanets and their atmospheres, including albedo in the visible region (see for example, Chubb et al. 2023; Krenn et al. 2023).

**(ii) Temperature-dependence:** As mentioned above, the main spectral features of aerosols which could be observed in exoplanet atmospheres using *JWST*, for example, may be shifted at high-temperatures in comparison to room-temperature (Zeidler et al. 2013, 2015), making high-temperature measurements of refractive indices of high priority. Zeidler et al. (2013, 2015) demonstrate the temperature-dependence of optical properties (i.e. refractive indices) of aerosol particles such as enstatite and olivine which are expected in typical hot gas giant exoplanets. Zeidler et al. (2015) demonstrate the vibrational bands of olivine and enstatite becoming broader and redshifted with increasing temperature; see for example, their figs 2–7. It is assumed a similar effect will be found in other species, however there are no available studies on temperature dependence for the vast majority of refractive indices and most are therefore only available for terrestrial temperatures. Obtaining newer high temperature data for a wide range of other species important in higher temperature atmospheres would greatly aid in the analysis of observed spectra from missions such as *JWST* and *Ariel*. Reflectance spectra and

refractive indices above 500–600 K are also important for surfaces, but at such high temperatures are usually not available.

For ices, important for characterizing cooler solar system bodies, three types of temperature effects are usually considered (Schmitt et al. 1998): (i) Phase transitions upon heating, irreversible for some cases, can lead to profound changes in spectral features. (2) Thermal annealing, also associated with irreversible reorganization of amorphous to more ordered structure resulting in a progressive shift and narrowing of the bands. (3) Thermal contraction of the crystal and phonon-phonon interactions can lead to a change of the spectroscopic bands with a complex temperature behaviour depending on many factors including the solid structure, phase etc, with the bandwidth generally increasing on heating. This change is reversible upon temperature.

For cooler Solar system conditions, the temperature dependence of the refractive index have been studied, e.g. by Warren & Brandt (2008) and Biermann, Luo & Peter (2000). The temperature dependence of the refractive index of H₂O has been investigated recently by He et al. (2022c) between 30 K and 160 K (see their fig. 7). These results for water and other small molecules are available in the Leiden Ice Data base for Astrochemistry–LIDA (Rocha et al. 2022). From what we know about ices, there is a clear temperature dependence which for H₂O results in a roughly 10 per cent increase from 30 K to 160 K (He et al. 2022c).

Like for the majority of the data types considered in this work, more laboratory studies, especially at warmer conditions, are needed for refractive indices.

**(iii) A guide on what tools to use:** computing cloud opacities from refractive index data requires some additional assumptions, including the shape of the cloud particles, and whether the particles are mixed composition. For example, Mie computations are fast but only applicable for spherical particles, while the discrete dipole approximation (DDA) (Draine & Flatau 1994) or T-matrix method (Mackowski & Mishchenko 1996) are very accurate for non-spherical particles which can be composed of multiple materials, but also computationally expensive. There are some methods which lie between these extremes, allowing for the computation of scattering and absorption coefficients for non-spherical particle shapes, at a lower cost but typically also lower accuracy that DDA. For example the distribution of hollow spheres (DHS) method (Min et al. 2005) is thought to be a good compromise between accuracy and speed, with the option to choose a degree of irregularity of the particles. The DHS method has been used in studies such as Dyrek et al. (2024). There is also the modified mean field theory (MMF) method, designed for computing opacities of dust aggregates (Tazaki & Tanaka 2018). Photochemical hazes are well described by mean field approximations, as the solid particles produced by photochemistry coagulate at relatively high pressures in the atmosphere and form fractal aggregates of spherical particles (Botet, Rannou & Cabane 1997). The fractal meanfield scattering code of Botet et al. (1997),
<table><caption>**Table 13.** Photodissociation thresholds and threshold products in the Leiden database (Heays et al. 2017; Hrodmarsson & Van Dishoeck 2023).</caption><thead><tr><th>Species</th><th>Photodis. products</th><th>Thresh Photoion</th><th>Species</th><th>Photodis. products</th><th>Thresh Photoion</th><th>Species</th><th>Photodis. products</th><th>Thresh Photoion</th></tr></thead><tbody><tr><td>AlH</td><td>Al + H</td><td>392</td><td>CO<sub>2</sub></td><td>CO + O</td><td>227</td><td>NH<sub>2</sub>CHO</td><td>NH<sub>3</sub> + CO</td><td>208</td></tr><tr><td>C<sub>2</sub></td><td>C + C</td><td>193</td><td>CS</td><td>C + S</td><td>168</td><td>NH<sub>3</sub></td><td>NH + H<sub>2</sub></td><td>301</td></tr><tr><td>C<sub>2</sub>H</td><td>C<sub>2</sub> + H</td><td>253</td><td>CS<sub>2</sub></td><td>CS + S</td><td>278</td><td>NO</td><td>N + O</td><td>191</td></tr><tr><td>C<sub>2</sub>H<sub>2</sub></td><td>C<sub>2</sub>H + H</td><td>217</td><td>H<sub>2</sub></td><td>H + H</td><td>274</td><td>NO<sub>2</sub></td><td>NO + O</td><td>398</td></tr><tr><td>C<sub>2</sub>H<sub>3</sub></td><td>H<sub>2</sub>CC + H</td><td>1240</td><td>H<sub>2</sub><sup>+</sup></td><td>H + H<sup>+</sup></td><td>290</td><td>NaCl</td><td>Na + Cl</td><td>293</td></tr><tr><td>C<sub>2</sub>H<sub>4</sub></td><td>C<sub>2</sub>H<sub>2</sub> + H<sub>2</sub></td><td>258</td><td>H<sub>2</sub>CO</td><td>H<sub>2</sub> + CO</td><td>361</td><td>NaH</td><td>Na + H</td><td>606</td></tr><tr><td>C<sub>2</sub>H<sub>5</sub></td><td>C<sub>2</sub>H<sub>4</sub> + H</td><td>260</td><td>H<sub>2</sub>CS</td><td>H<sub>2</sub> + CS</td><td>313</td><td>O<sub>2</sub></td><td>O + O</td><td>242</td></tr><tr><td>C<sub>2</sub>H<sub>5</sub>OH</td><td>C<sub>2</sub>H<sub>5</sub>O + H</td><td>208</td><td>H<sub>2</sub>O</td><td>H + OH</td><td>242</td><td>O<sub>2</sub><sup>+</sup></td><td>O + O<sup>+</sup></td><td>186</td></tr><tr><td>C<sub>2</sub>H<sub>6</sub></td><td>C<sub>2</sub>H<sub>5</sub> + H</td><td>287</td><td>H<sub>2</sub>O<sub>2</sub></td><td>OH + OH</td><td>556</td><td>O<sub>3</sub></td><td>O<sub>2</sub> + O</td><td>1180</td></tr><tr><td>C<sub>3</sub></td><td>C<sub>2</sub> + C</td><td>268</td><td>H<sub>2</sub>S</td><td>H + SH</td><td>318</td><td>OCS</td><td>CO + S</td><td>280</td></tr><tr><td>C<sub>3</sub>H<sub>3</sub></td><td>C<sub>3</sub>H<sub>2</sub> + H</td><td>300</td><td>HC<sub>3</sub>H</td><td>I-C<sub>3</sub>H + H</td><td>400</td><td>OH</td><td>O + H</td><td>279</td></tr><tr><td>C<sub>3</sub>H<sub>7</sub>OH</td><td>C<sub>3</sub>H<sub>7</sub>O + H</td><td>208</td><td>HC<sub>3</sub>N</td><td>H + C<sub>3</sub>N</td><td>244</td><td>OH<sup>+</sup></td><td>O<sup>+</sup> + H</td><td>247</td></tr><tr><td>CH</td><td>C + H</td><td>358</td><td>HCN</td><td>H + CN</td><td>243</td><td>PH</td><td>P + H</td><td>398</td></tr><tr><td>CH<sup>+</sup></td><td>C + H<sup>+</sup></td><td>303</td><td>HCO</td><td>H + CO</td><td>2037</td><td>PH<sup>+</sup></td><td>P<sup>+</sup> + H</td><td>369</td></tr><tr><td>CH<sub>2</sub></td><td>C + H</td><td>417</td><td>HCO<sup>+</sup></td><td>H + CO<sup>+</sup></td><td>145</td><td>S<sub>2</sub></td><td>S + S</td><td>283</td></tr><tr><td>CH<sub>2</sub><sup>+</sup></td><td>CH<sup>+</sup> + H</td><td>204</td><td>HCOOH</td><td>H + HCOO</td><td>333</td><td>SH</td><td>S + H</td><td>345</td></tr><tr><td>CH<sub>3</sub></td><td>CH + H<sub>2</sub></td><td>271</td><td>HCl</td><td>H + Cl</td><td>279</td><td>SH<sup>+</sup></td><td>S<sup>+</sup> + H</td><td>320</td></tr><tr><td>CH<sub>3</sub>CHO</td><td>HCO + CH<sub>3</sub></td><td>342</td><td>HCl<sup>+</sup></td><td>H + Cl<sup>+</sup></td><td>267</td><td>SO</td><td>S + O</td><td>233</td></tr><tr><td>CH<sub>3</sub>OCH<sub>3</sub></td><td>CH<sub>3</sub>O + CH<sub>3</sub></td><td>196</td><td>HF</td><td>H + F</td><td>211.4</td><td>SO<sub>2</sub></td><td>O + SO</td><td>219</td></tr><tr><td>CH<sub>3</sub>OCHO</td><td>HCO + CH<sub>3</sub></td><td>248</td><td>HNC</td><td>H + CN</td><td>165</td><td>SiH</td><td>Si + H</td><td>417</td></tr><tr><td>CH<sub>3</sub>CN</td><td>CH<sub>2</sub>CN + H</td><td>308</td><td>HNCO</td><td>NH + CO</td><td>333</td><td>SiH<sup>+</sup></td><td>Si<sup>+</sup> + H</td><td>391</td></tr><tr><td>CH<sub>3</sub>NH<sub>2</sub></td><td>CH<sub>3</sub>NH + H</td><td>244</td><td>HO<sub>2</sub></td><td>H + O<sub>2</sub></td><td>476</td><td>SiO</td><td>Si + O</td><td>150</td></tr><tr><td>CH<sub>3</sub>OH</td><td>CH<sub>3</sub>O + H</td><td>280</td><td>LiH</td><td>Li + H</td><td>579</td><td>c-C<sub>3</sub>H</td><td>C<sub>3</sub> + H</td><td>289</td></tr><tr><td>CH<sub>3</sub>SH</td><td>CH<sub>3</sub>S + H</td><td>331</td><td>MgH</td><td>Mg + H</td><td>623</td><td>c-C<sub>3</sub>H<sub>2</sub></td><td>c-C<sub>3</sub>H + H</td><td>284</td></tr><tr><td>CH<sub>4</sub></td><td>CH<sub>3</sub> + H</td><td>277</td><td>N<sub>2</sub></td><td>N + N</td><td>127</td><td>SiO</td><td>Si + O</td><td>150</td></tr><tr><td>CH<sub>4</sub><sup>+</sup></td><td>CH<sub>3</sub><sup>+</sup> + H</td><td>1031</td><td>N2O</td><td>N<sub>2</sub> + O</td><td>735</td><td>I-C<sub>3</sub>H</td><td>C<sub>3</sub> + H</td><td>379</td></tr><tr><td>CN</td><td>C + N</td><td>160</td><td>NH</td><td>N + H</td><td>362</td><td>I-C<sub>3</sub>H<sub>2</sub></td><td>I-C<sub>3</sub>H + H</td><td>320</td></tr><tr><td>CO</td><td>C + O</td><td>110</td><td>NH<sup>+</sup></td><td>N<sup>+</sup> + H</td><td>281</td><td>I-C<sub>4</sub></td><td>C<sub>3</sub> + C</td><td>263</td></tr><tr><td>CO<sup>+</sup></td><td>C<sup>+</sup> + O</td><td>149</td><td>NH<sub>2</sub></td><td>NH + H</td><td>314</td><td>I-C<sub>4</sub>H</td><td>C<sub>4</sub> + H</td><td>267</td></tr><tr><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>I-C<sub>5</sub>H</td><td>C<sub>5</sub> + H</td><td>348</td></tr></tbody></table>

<table><caption>**Table 14.** Ionisation thresholds in the Leiden database (Heays et al. 2017; Hrodmarsson & Van Dishoeck 2023).</caption><thead><tr><th>Species</th><th>Thresh Photoion</th><th>Species</th><th>Thresh Photoion</th><th>Species</th><th>Thresh Photoion</th><th>Species</th><th>Thresh Photoion</th></tr></thead><tbody><tr><td>Al</td><td>207</td><td>CH<sub>3</sub>OH</td><td>113</td><td>HCO</td><td>152</td><td>O<sub>2</sub></td><td>103</td></tr><tr><td>AlH</td><td>156</td><td>CH<sub>3</sub>SH</td><td>131</td><td>HCOOH</td><td>110</td><td>O<sub>3</sub></td><td>99</td></tr><tr><td>C</td><td>110</td><td>CH<sub>4</sub></td><td>98</td><td>HCl</td><td>97</td><td>OCS</td><td>111</td></tr><tr><td>C<sub>2</sub></td><td>102</td><td>CN</td><td>87</td><td>HF</td><td>77.5</td><td>OH</td><td>95</td></tr><tr><td>C<sub>2</sub>H</td><td>107</td><td>CO</td><td>88</td><td>HNC</td><td>103</td><td>P</td><td>118</td></tr><tr><td>C<sub>2</sub>H<sup>-</sup></td><td>412</td><td>CO<sub>2</sub></td><td>90</td><td>HNCO</td><td>108</td><td>PH</td><td>122</td></tr><tr><td>C<sub>2</sub>H<sub>2</sub></td><td>109</td><td>CS</td><td>110</td><td>HO<sub>2</sub></td><td>109</td><td>Rb</td><td>297</td></tr><tr><td>C<sub>2</sub>H<sub>3</sub></td><td>147</td><td>CS<sub>2</sub></td><td>123</td><td>K</td><td>286</td><td>S</td><td>120</td></tr><tr><td>C<sub>2</sub>H<sub>4</sub></td><td>118</td><td>Ca</td><td>203</td><td>Li</td><td>230</td><td>S<sub>2</sub></td><td>133</td></tr><tr><td>C<sub>2</sub>H<sub>5</sub></td><td>153</td><td>Ca<sup>+</sup></td><td>104</td><td>LiH</td><td>152</td><td>SH</td><td>119</td></tr><tr><td>C<sub>2</sub>H<sub>5</sub>OH</td><td>119</td><td>Cl</td><td>96</td><td>Mg</td><td>162</td><td>SO</td><td>120</td></tr><tr><td>C<sub>2</sub>H<sub>6</sub></td><td>108</td><td>Co</td><td>158</td><td>Mn</td><td>167</td><td>SO<sub>2</sub></td><td>100</td></tr><tr><td>C<sub>3</sub></td><td>102</td><td>Cr</td><td>183</td><td>N</td><td>85</td><td>Si</td><td>152</td></tr><tr><td>C<sub>3</sub>H<sub>3</sub></td><td>143</td><td>Fe</td><td>158</td><td>N<sub>2</sub></td><td>79</td><td>SiH</td><td>156</td></tr><tr><td>C<sub>3</sub>H<sub>7</sub>OH</td><td>123</td><td>H</td><td>91</td><td>N<sub>2</sub>O</td><td>96</td><td>SiO</td><td>108</td></tr><tr><td>C<sub>2</sub>H<sup>-</sup></td><td>348</td><td>H<sup>-</sup></td><td>1470</td><td>NH</td><td>92</td><td>Ti</td><td>182</td></tr><tr><td>C<sub>6</sub>H<sup>-</sup></td><td>327</td><td>H<sub>2</sub></td><td>82</td><td>NH<sub>2</sub></td><td>111</td><td>Zn</td><td>132</td></tr><tr><td>CH</td><td>117</td><td>H<sub>2</sub>CO</td><td>114</td><td>NH<sub>2</sub>CHO</td><td>121</td><td>c-C<sub>3</sub>H</td><td>129</td></tr><tr><td>CH<sub>2</sub></td><td>119</td><td>H<sub>2</sub>CS</td><td>135</td><td>NH<sub>3</sub></td><td>122</td><td>c-C<sub>3</sub>H<sub>2</sub></td><td>136</td></tr><tr><td>CH<sub>3</sub></td><td>126</td><td>H<sub>2</sub>O</td><td>98</td><td>NO</td><td>134</td><td>I-C<sub>3</sub>H</td><td>136</td></tr><tr><td>CH<sub>3</sub>CHO</td><td>121</td><td>H<sub>2</sub>O<sub>2</sub></td><td>117</td><td>NO<sub>2</sub></td><td>127</td><td>I-C<sub>3</sub>H<sub>2</sub></td><td>119</td></tr><tr><td>CH<sub>3</sub>OCH<sub>3</sub></td><td>127</td><td>H<sub>2</sub>S</td><td>119</td><td>Na</td><td>241</td><td>I-C<sub>4</sub></td><td>116</td></tr><tr><td>CH<sub>3</sub>OCHO</td><td>113</td><td>HC<sub>3</sub>H</td><td>139</td><td>NaH</td><td>179</td><td>I-C<sub>4</sub>H</td><td>129</td></tr><tr><td>CH<sub>3</sub>CN</td><td>102</td><td>HC<sub>3</sub>N</td><td>107</td><td>Ni</td><td>162</td><td>I-C<sub>5</sub>H</td><td>168</td></tr><tr><td>CH<sub>3</sub>NH<sub>2</sub></td><td>135</td><td>HCN</td><td>91</td><td>O</td><td>91</td><td>—</td><td>—</td></tr></tbody></table>
available on GitHub,<fnref n="60" /> can be used to get the optical properties of fractal aggregates using refractive indices and fractal dimension as inputs. A recent study by Lodge et al. (2023) investigates the differences which result in the use of the Mie, MMF, and DDA approaches, and demonstrates that using a lower resolution for DDA can also be a good compromise in terms of accuracy and speed. There has been a call amongst some users of atmospheric modelling and retrieval codes for some simple tools to create and read-in cloud opacities. There are two approaches here: either the code reads in refractive indices and particle sizes, and uses Mie theory or similar (DHS is available as part of the ARCIS (Min et al. 2020) code, for example) to compute opacities on the fly; or pre-computed grids of opacities for various species, particle sizes, and shapes, can be read in. Codes such as YunMa (Ma et al. 2023) assume spherical particle sizes. For surfaces, simple linear mixing of pure component spectra can be easily surpassed by more advanced radiative transfer models, such as Hapke's (Hapke 1993). The effect of the surface texture (compacity, roughness, and grain size) should be also taken into account if the actual data contains sufficient information to retrieve these parameters.

(iv) **Amorphous versus crystalline forms:** there is some discussion in the literature over whether crystalline or amorphous structures are expected under certain situations. It is thought that condensates are typically crystalline if their condensation time-scale is faster than their annealing time-scale, and amorphous if the opposite is true. This is because in the former case the condensates can rearrange quickly enough to form the crystalline structure, but if the condensation occurs much faster than the formed condensate will be amorphous (Gail & Sedlmayr 1999; Herbort et al. 2020). Grant et al. (2023) demonstrate the impact of choosing either amorphous or crystalline structures for their refractive indices of solid-state SiO₂, with some resulting small differences between their retrieved spectra of WASP-17 b. It is therefore important for refractive indices for both crystalline and amorphous forms to be available for models. Helling (2021) calls for opacity data for condensates across large wavelengths ranges and crystalline species, along with opacity modelling for non-spherical and charged cloud particles.

(v) **Additional species:** it has been noted by members of the community that sulphuric acid-based aerosols are of interest but are missing from the list of available aerosol refractive indices.

(vi) **Condensation curves:** these are required for all species in order to correctly predict their presence in exoplanet atmospheres, over a large range of pressures and temperatures. As mentioned above, fig. 4 of Gail et al. (2013) illustrates the considerable difference between condensation curves of SiO using different measured data, with the newer measurements allowing SiO to be present in solid form up to higher temperatures than the older ones. This highlights the importance of laboratory data for cloud modelling.

(vii) **In-situ measurements of reflectance:** as noted in this section, these data are available from different sources (see Table 14), but not available in one easily accessible data base.

(viii) **Cloud nucleation parameters:** constraints on the surface tension, contact angles, and similar parameters are required for the description of cloud nucleation (Arfaux & Lavvas 2024). These are typically used even for non-molecular condensates, which are characterized by the absence of the corresponding condensing species in the gas phase e.g. MgSiO₃ and Na₂S (Arfaux & Lavvas 2024), thus a better understanding of the heterogeneous mechanisms at play for these cases is also needed.

(ix) **Laboratory data to aid understanding of the coupling of haze and clouds:** it is becoming more evident with the latest observations that the coupling of haze and clouds is important (see for example, Arfaux & Lavvas 2024). To better understand this coupling, experiments that investigate the potential of haze particles acting as nucleation sites for the condensation of anticipated condensates in exoplanet atmospheres are needed.

# 7 ATMOSPHERIC CHEMISTRY

Data required for atmospheric chemistry models appropriate for exoplanet atmospheres primarily include thermochemical data for equilibrium networks, and collision and reaction rates for kinetic networks.

## 7.0.1 Equilibrium chemistry

There are a variety of codes designed to model chemical equilibrium in atmospheres, including exoplanet atmospheres. For example, ACE (Agúndez et al. 2012, 2020), FastChem<fnref n="61" /> (Stock et al. 2018; Stock, Kitzmann & Patzer 2022), and GGchem<fnref n="62" /> (Woitke et al. 2018) all work by minimizing the Gibbs free energy of the system for a given pressure and temperature. The minimization of Gibbs free energy requires thermochemical data as input. Many codes make use of the algorithm of Gordon & Mcbride (1994) to compute the free energy minimization.

Here we give a little more context to what data are required for codes such as these. Each species is characterized by two sets of thermochemical coefficients, that can be in a 7-format or in a 9-format. The 9-format, being a more recent one, permits more accurate calculations. There is a set for the 'high-temperature' range and another one for the 'low-temperature' range. For each of these ranges, thermochemical properties of each species in the normal conditions enthalpy ($h^0$) and entropy ($s^0$) can be calculated thanks to the NASA polynomials. For the 7-format, the expressions are:

$$ \frac{h^0(T)}{RT} = a_1 + \frac{a_2 T}{2} + \frac{a_3 T^2}{3} + \frac{a_4 T^3}{4} + \frac{a_5 T^4}{5} + \frac{a_6}{T} \quad (7) $$

$$ \frac{s^0(T)}{R} = a_1 \ln T + a_2 T + \frac{a_3 T^2}{2} + \frac{a_4 T^3}{3} + \frac{a_5 T^4}{4} + a_7. \quad (8) $$

The polynomials corresponding to the 9-format are slightly different:

$$ \frac{h^0(T)}{RT} = -a_1 T^{-2} + a_2 T^{-1} \ln T + a_3 + \frac{a_4 T}{2} + \frac{a_5 T^2}{3} + \frac{a_6 T^3}{4} + \frac{a_7 T^4}{5} + \frac{b_1}{T} \quad (9) $$

$$ \frac{s^0(T)}{R} = -\frac{a_1 T^{-2}}{2} - a_2 T^{-1} + a_3 \ln T + a_4 T + \frac{a_5 T^2}{2} + \frac{a_6 T^3}{3} + \frac{a_7 T^4}{4} + b_2. \quad (10) $$

With these two quantities calculated, the Gibbs Free Energy of the system can be calculated:

$$ G_{\text{sys}} = \sum_{l}^{L} \left( h_{l}^{0}(T) - T s_{l}^{0}(T) + RT \ln \frac{P}{P^{0}} + RT \ln N_{l} \right) \times N_{l} \quad (11) $$

The sets of $N_l$ that will minimize this equation (the Gibbs Free Energy of the system) will correspond to the composition at thermochemical equilibrium. Some data bases which gather equilibrium

<footnote n="60">⁶⁰https://github.com/storyofthewolf/fractal_optics_coreshell.git</footnote>

<footnote n="61">⁶¹https://github.com/exoclime/FastChem</footnote>

<footnote n="62">⁶²https://github.com/pw31/GGchem</footnote>
computation of electronically excited states are also utilised
by Sršeň & Slaviček (2021) and Sršeň et al. (2023).

Collections of theoretical UV cross-sections of PAHs are
available online<sup>73</sup> (Malloci et al. 2007) and measurements of
the cross sections of cationic PAHs (Zhen et al. 2016; Wenzel
et al. 2020) have been used recently by Berné et al. (2022)
to show the importance of up-to-date experimental measure-
ments to understand the contribution of the ionization of PAH
cations to a variety of environments.

The ExoMol database has started providing photoabsorp-
tion and photodissociation data, see Tennyson et al. (2023).
This will include UV line lists (bound-bound transitions
only), UV photoabsorption cross-sections (bound-continuum
only) and separately full temperature dependent photoabsorp-
tion cross-sections (all together). The data are produced with
essentially the same computational ExoMol-methodology
used for production of line lists, see Section 2.1.1.2, i.e. a
mixture of first-principle quantum-mechanical calculations
and empirical refinement, see Pezzella et al. (2021). As in
the case of the line lists, the advantage of theoretical spectra
is in the completeness at higher temperatures. ExoMol also
provides hosting for experimental VUV/UV cross-sections
of molecules, see Fateev et al. (2025). The recent AloHa
AlH line list gives an example of the treatment of mixed
spectral lines and continuum absorption in UV (Yurchenko
et al. 2024b) while Pezzella et al. (2022) gives an example of
photodissociation cross-sections.

## 8.2 What's being worked on?

(i) ExoMol is working on producing temperature-
dependent UV photoabsorption and photodissociation data
for a variety of molecules including OH, SO and HCN.

(ii) Recently, acetylene has been measured and will be published soon (Fleury et al. in prep), as part of the EXACT project<sup>74</sup>, where UV absorption cross-sections are measured experimentally. The EXACT project aims to measure UV absorption cross-sections at high temperatures for many species, using their experimental setup at LISA.

(iii) VUV photoabsorption cross-sections and VUV Photoionization cross-sections are currently being studied for a number of molecules at the DESIRS beamline at the SOLEIL synchrotron facility in France. Current efforts involve sulphur-containing species; photoabsorption cross-section of CS and C₂ will be measured and the photoionization cross-sections of sulphur containing radicals such as CCS will be investigated.

(iv) PAHs: similar to the recent experimental work of Chowdhury et al. (2024) on the cyanonaphthalenes, the photoionization of a large sample of neutral PAHs of varying sizes and symmetries are being investigated (Hrodmarsson et al. in prep) to improve previous experimental data on PAH photoabsorption data from the 1990's (Verstraete et al. 1990; Tobita et al. 1994; Jochims et al. 1996; Jochims et al. 1997, 1999) currently in use to describe the contribution of neutral PAHs to the gas heating in the ISM.

8.3 Data needs: What's missing and urgent?

(i) **Photoabsorption cross-sections at high temperatures:** Fortney et al. (2019) (a collaborative Astro2020 Science White Paper) expressed the need for photoabsorption cross-sections measurements and their temperature dependency in the VUV wavelength range (115 - 230 nm) for molecules such as N₂, O₂, O₃, H₂O, CO, CO₂, CH₄, NH₃, TiO, VO, HCN, C₂H₂, H₂S, PH₃.

9 DATA STANDARDS

**9.1 Principles of and need for data standards**

Standards for the description of atomic and molecular data are
crucial for their effective use in the study of exoplanet atmo-
spheres. The successful adoption of a set of standards for the
description of species, quantum states and spectroscopic pro-
cesses facilitates interoperability between simulation codes,
data exchange between researchers, the long-term preserva-
tion of data and automatic processing of data, for example
through Application Programming Interfaces (APIs).

In any discussion about data standards a distinction must
be made between metadata and data formats. All datasets
are associated, implicitly or explicitly, with some set of con-
ventions for describing metadata: the context, applicability,
provenance, physical units and additional information which
give meaning to the raw numbers of the data themselves.
The format in which these numerical data are provided
may vary between databases (depending, for example, on
the size of the data sets). However, an interoperable ecosystem of databases must adopt standardised, unambiguous and
machine-readable metadata conventions to allow comparison
and aggregation across data sets.

Modern database services aspire to conform to so-called
FAIR principles of data curation (Wilkinson et al. 2016); in
brief, FAIR data are:

* Findable: associated with globally unique and persistent identifiers; registered in a searchable, online resource.

* Accessible: retrievable through a standardised protocol.

* Interoperable: associated with metadata in a formal, well-described representation.

* Reusable: clearly-licensed and documented, with detailed provenance and rich metadata attributes providing context to the data.

In the context of atomic and molecular data, the adoption
of these principles can be realised as follows

* Atomic and molecular species and states should be specified using common identifiers.

* Data sets, such as line lists and cross-sections, should be assigned globally unique identifiers and be locatable through these identifier using online services.

* Where data sets are updated, older versions should be permanently available, identified and time-stamped.

* All numerical data should be associated with clear meta-
  data describing its format, physical units and limits of appli-
  cability (for example, valid temperature or pressure range).

* Digital Object Identifiers (DOIs) should be attached to data sets to permanently identify the origin of the data; typically such DOIs will refer to the data's associated publication, such as a journal article.

<footnote n="73">73 http://astrochemistry.ca.astro.it/database/pahs.html</footnote>

<footnote n="74">⁷⁴ https://www.anr-exact.cnrs.fr/</footnote>
thermodynamic coefficients for large sets of species are listed in Section 7.1.1.

### 7.0.2 Disequilibrium chemistry

In addition to equilibrium chemistry, some codes include disequilibrium chemistry processes (i.e. quenching induced by vertical mixing, photodissociation, condensation and evaporation, surface deposition, outgassing, and atmospheric escape) that control the atmospheric chemical composition (i.e. García Muñoz 2007; Moses et al. 2011; Venot et al. 2012, 2020; Tremblin et al. 2016; Rimmer & Helling 2016; Tsai et al. 2017; Al-Refaie et al. 2022). These codes require data adapted to the type of exoplanets studied. For instance, for warm exoplanets, such as hot Jupiters, data which are validated/measured/calculated at high temperatures are necessary. The data are: chemical schemes, that is to say a list of hundreds to thousands of reactions, with the associated reaction rates, but also physicochemical data for photodissociations (UV absorption cross-sections and quantum yields). Thermodynamic data are also necessary to calculate reverse reactions rates.

## 7.1 State-of-the-art – data availability

### 7.1.1 Equilibrium chemistry

The C/H/O/N (CHON) chemical species included in ACE (Agúndez et al. 2012, 2020) are mainly sourced from Gordon & Mcbride (1994), Atkinson et al. (2004, 2006, 2007) and Bounaceur et al. (2010), with some constants computed using THERGAS (Muller et al. 1995). ACE is commonly used in conjunction with chemical kinetic models by Venot et al. (2012, 2020) and in the more recently re-written Python version FRECKLL (Al-Refaie et al. 2022). FastChem (Stock et al. 2018, 2022) sources their thermochemical data from the NIST-JANAF data base (Chase 1998).<fnref n="63" /> A version of FastChem has recently been released which includes equilibrium condensation and rainout (Kitzmann, Stock & Patzer 2023), and comes with a python interface. GGchem (Woitke et al. 2018) uses multiple sources of thermochemical data, with new species continuously added to the data files on the GitHub page. The sources for their chemical data comes from Barklem & Collet (2016), NIST-JANAF (Chase 1998) and Tsuji (1973). Appendix C of Woitke et al. (2018) includes a comparison of the molecular equilibrium constants collected from various sources; the full comparison data can be found at Worters et al. (2017). A graphical comparison of mineral Gibbs free energy data can also be found at Woitke et al. (2017). Other sources of thermochemical data include the Burcat data base (Burcat et al. 2005)<fnref n="64" /> and Sharp & Huebner (1990). Recently, Wang et al. (2023a) provided NASA polynomials for 464 molecules with an emphasis on those species which are important for exoplanets.

### 7.1.2 Disequilibrium chemistry

Concerning chemical schemes, one major difficulty is to get data corresponding to the temperature of exoplanets. Due to long-standing studies of Solar System bodies, most of the chemical schemes developed for planetology are valid for low-temperature medium (e.g. Atreya & Gu 1994; Moses et al. 2005, 2018, Dobrijevic et al. 2014, 2016). However, in the combustion domain, many studies regarding

chemical kinetics at high temperatures have been performed in the last few decades and can be relevant for modelling the atmospheres of exoplanets (e.g. Cathonnet, Boettner & James 1982; Kéromnès et al. 2013; Burke et al. 2016). Based on this fact, Venot et al. (2012, 2015, 2020) and Veillet et al. (2024) have developed and updated reliable chemical schemes validated through combustion experiments for CHON species. These schemes have been validated on a large range of temperatures and pressures, typically 500–2500 K and 10⁻⁶–100 bar, thanks to comparisons with many experimental data sets (Veillet et al. 2024). This range of validation is probably adequate, as the chemical composition of the upper part of the atmosphere, which might in certain cases be at lower temperatures than 500 K, is mainly governed by photolysis processes and/or vertical mixing-induced quenching. Even if those have not been validated with the same methodology as the aforementioned ones, chemical schemes with sulphur have also been developed and presented in various studies, such as Zahnle et al. (2016), Tsai et al. (2021), and Hobbs et al. (2021). The network of Tsai et al. (2021) was recently used to contextualize the photochemically produced SO₂ feature detection in the atmosphere of hot Jupiter WASP-39 b (Tsai et al. 2023). Other disequilibrium chemical schemes tailored for exoplanets include the STAND network (Rimmer & Helling 2016), which includes effects of UV photochemistry, cosmic ray chemistry, and lightning-driven chemistry, and the network of Moses et al. (2010) which uses thermochemical/photochemical kinetics and transport models. ExoREL (Hu 2019, 2021) is a code to compute disc-averaged reflected-light spectra at any planetary phase, taking into account the radiative properties of clouds and their feedback on planetary albedo.

Data bases such as the JPL Chemical Kinetics and Photochemical Data data base,<fnref n="65" /> the KInetic Database for Astrochemistry (KIDA) data base<fnref n="66" /> (Wakelam et al. 2012), the NIST chemical kinetic data base,<fnref n="67" /> and the UMIST Data base for Astrochemistry (UDfA)<fnref n="68" /> (Milar et al. 2024) are all sources of kinetic and photochemical data. KIDA provides evaluations of reaction rates and uncertainties, and contains chemical schemes used in planetology. Concerning UV photochemical data, very few data exist at high temperatures. Venot et al. (2013, 2018b) studied experimentally the thermal dependence of carbon dioxide and its impact on atmospheric composition and Fleury et al. (2023a) studied the one of C₂H₂. Grosch, Fateev & Clausen (2015) measured UV absorption cross-sections of sulphur-containing compounds at elevated temperatures. See Section 8 for details on UV photodissociation and photoabsorption cross-sections.

## 7.2 Data used by retrieval codes

### 7.2.1 Equilibrium chemistry

Many of the aforementioned equilibrium chemistry codes are included in various atmospheric modelling and retrieval codes, such as TauREx3 (Al-Refaie et al. 2021) where ACE is included as standard with the option to include others using the plugin feature of TauREx 3.1, and ARCIS (Ormel & Min 2019; Min et al. 2020), where GGchem (Woitke et al. 2018) is fully integrated. FastChem (Stock et al. 2018, 2022) is the main chemical model used in the HELIOS radiative transfer code (Malik et al. 2017; Kitzmann

<footnote n="63">⁶³https://janaf.nist.gov/janbanr.html</footnote>

<footnote n="64">⁶⁴http://garfield.chem.elte.hu/Burcat/burcat.html</footnote>

<footnote n="65">⁶⁵https://jpldataeval.jpl.nasa.gov/index.html</footnote>

<footnote n="66">⁶⁶https://kida.astrochem-tools.org/</footnote>

<footnote n="67">⁶⁷https://kinetics.nist.gov/kinetics/</footnote>

<footnote n="68">⁶⁸http://udfa.ajmarkwick.net</footnote>
## 9.2 Focus on Interoperability: VAMDC and other services

The Virtual Atomic and Molecular Data Centres (VAMDC) consortium (VAMDC 2014-; Albert et al. 2020) is a group of research institutes that share a common technical and political framework for the distribution and curation of atomic and molecular data. The goal of the consortium is to build a software infrastructure allowing the linking and searching of multiple databases through a common data format (“XSAMS”) and query language (“VSS2”). The VAMDC has built a platform (the “portal”) for the simultaneous search of, at the time of writing, 38 databases and the aggregation of matching data sets. It now is concerned with further developing data standards, promoting collaboration in data production and evaluation, and in broadening its scope beyond spectroscopy and plasma physics. As machine learning matures and becomes more widely-adopted, the VAMDC is also exploring the application of artificial intelligence to its data services, to facilitate, for example, data transformation and visualization and to assist with the validation and aggregation of data.

An up-to-date summary of the current status of the VAMDC and its database nodes can be found in the report of its most recent annual meeting, held as a Technical Meeting of the International Atomic Energy Agency in November 2023 (Hill 2023).

## 9.3 What's being worked on?

The PyValem software library (PyValem 2024) provides a framework of Python routines for parsing standardised, text-based representations of atomic and molecular species and states; the syntax of these representations is largely adopted by the HITRAN and ExoMol databases, and is fully implemented in the CollisionDB database (Hill et al. 2024, accepted; CollisionDB 2024) of plasma collisional processes. In addition to providing a common language for referring to species and states, this library can also perform a degree of validation on its input, for example by ensuring that quantum numbers are valid and reactions conserve stoichiometry and charge.

Several of the resources described in this paper expose an API for the automated querying and retrieval of atomic and molecular data; in addition to the VAMDC portal service outlined above, there are well-documented APIs for the HITRAN, ExoMol and CollisionDB databases; the software tools for interacting with these APIs are either already available or under active development.

The MAESTRO database, introduced in Section 4.2, is under ongoing development. It aims to create one uniform and standardised database for opacities useful in the characterisation of exoplanet atmospheres, and to aid in the establishment of community standards for computing opacity data (see, e.g., Gharib-Nezhad et al. 2024).

## 9.4 Data standards needs: What's missing and urgent?

At the time of writing there are several competing standards for describing atomic and molecular species; whilst some of these are widely-adopted in some areas of chemistry and physics (for example, SMILES (Weininger 1988), or InChi / InChiKey strings (Heller et al. 2015)), there is no entirely satisfactory and easy-to-use standard for small molecules,

particularly metal hydrides, in the context of astrophysical spectroscopy.

As described above, the line lists required of even relatively small molecules for spectroscopic modelling at elevated temperatures can become very large, with over $10^{10}$ individual transitions contributing the observable spectra. Therefore, database services supporting the asynchronous retrieval of large data files are needed. New software tools are required for the visualization, validation, extraction and, potentially, reduction of such data sets. These tools should allow for the interconversion between popular data formats (XSAMS, HITRAN, ExoMol, and so on) and must be tested and maintained on a regular basis. In addition to improving usability, these tools can also perform the important function of validating the integrity of the data by checking for format consistency and data completeness.

A further issue is the complexity of the quantitative description of line broadening effects: this necessitates a flexible data model that can be applied to multiple databases; at present different approaches are taken by those databases that contain broadening data sets.

## 10 CONCLUSION

In this white paper we have focused on a number of different data types: molecular and atomic line lists (Section 2), molecular and atomic line shapes (Section 3), computed cross-sections and opacities (Section 4), collision induced absorption (CIA) and other continuum data (Section 5), data for aerosols (Section 6), data for atmospheric chemistry models (Section 7), and data for UV photodissociation or photoabsorption (Section 8). Needs for data standards, metadata, existing rules and associated tools were discussed in Section 9. For each data type we have focused on: data availability, what data is typically used by atmospheric modelling and retrieval codes, what is missing and urgent, and what is currently being worked on. Of course the information provided in this paper is not comprehensive, but we have tried to give an overview for each data type for both data users and data providers.

We also note that there are some important data topics relating to exoplanet atmospheres which we do not address in this particular paper. For example, accurate data regarding stellar spectra are important for photochemistry (see Section 7) and UV photodissociation processes (see Section 8). The secondary eclipse measurement of a transiting exoplanet gives information on the emission and reflection spectra. These measurements are presented as a ratio of planetary flux to stellar flux, and therefore also require an accurate stellar flux model. We note there are some resources for theoretical models of stellar spectra such as the Kurucz-Castelli grids of models$^{75}$ (Heiter et al. 2002; Castelli & Kurucz 2004), models from the SVO (Spanish Virtual Observatory)$^{76}$, which provides data for 70 collections of theoretical spectra and observational templates, and the PHOENIX stellar models$^{77}$ (Husser et al. 2013). The Virtual Planet Laboratory regroups data from various publications$^{78}$, and there are

<footnote>$^{75}$ http://kurucz.harvard.edu/grids.html</footnote>

<footnote>$^{76}$ http://svo2.cab.inta-csic.es/theory/newov2/index.php</footnote>

<footnote>$^{77}$ https://phoenix.astro.physik.uni-goettingen.de/</footnote>

<footnote>$^{78}$ https://vpl.uw.edu/models/spectral-database-tools/</footnote>
et al. 2020). Other codes such as petitRADTRANS (Mollière et al. 2019) and Exo-REM (Baudino et al. 2015) include self-written chemical routines. For example, easyCHEM is a self-written Gibbs free energy minimizer which is included in petitRADTRANS and described in Mollière et al. (2017). Al-Refaie et al. (2022) compares computed atmospheric spectra of a hot gas giant exoplanet using some of the available equilibrium chemistry codes plugged into TauREx3; ACE (Agúndez et al. 2012, 2020), FastChem (Stock et al. 2018), GGchem (Woitke et al. 2018), and also GGchem with condensation included. Jaziri et al. (2024) explore the reliability of 1D equilibrium chemistry models to predict the composition of 3D exo-atmospheres with simulated transmission spectra of *Ariel* and *JWST* observations. They use the ACE chemical scheme in the TauREx retrieval code. ATMO (Tremblin et al. 2015; Tremblin et al. 2016) is a radiative/convective equilibrium code which is coupled to the CHNO-based chemical network of Venot et al. (2012).

### 7.2.2 Disequilibrium chemistry

In addition to equilibrium chemistry, some atmospheric retrieval codes explicitly parametrize the disequilibrium chemistry process of quenching. For example, Kawashima & Min (2021) outlines the disequilibrium chemistry scheme included in atmospheric retrieval and modelling code ARCIS (Ormel & Min 2019; Min et al. 2020), which includes vertical mixing of CH₄, CO, H₂O, NH₃, N₂, and CO₂. ARCIS uses thermochemical data collated as part of GGchem (mentioned above; Woitke et al. 2018), along with some extra species sourced from Burcat et al. (2005) and Ruscic et al. (2005). Recently, a step forward has been made with TauREx3 using the kinetic code FRECKLL (Full and Reduced Exoplanet Chemical Kinetics distiLLed) as a plugin (Al-Refaie et al. 2022), in order to perform retrievals taking into account chemical kinetics. The kinetic network included in FRECKLL is based on the chemical scheme of Venot et al. (2020). VULCAN is another open source chemical kinetics code for exoplanetary atmospheres, written in python (Tsai et al. 2017). Their paper describes the rate coefficients and thermodynamic data used in the network. ATMO (Tremblin et al. 2015; Tremblin et al. 2016) includes a kinetic chemical network, as well as the equilibrium chemistry network described above. HyDRA (Gandhi & Madhusudhan 2017) is a disequilibrium retrieval framework for thermal emission spectra of exoplanetary atmospheres.

## 7.3 What's being worked on?

A chemical scheme validated through combustion experiment coupling CHON species to sulphur species is currently in progress and should be published in 2024 (Veillet et al., in preparation). There is currently work underway to include P and S in CHON chemical schemes, using ab initio methods. Sticking coefficients of gas phase molecules onto solid atmospheric haze are currently in progress and should be published in 2024 (Perrin et al., in preparation). Several UV absorption cross sections (NH₃, HCN, SO₂) are currently under study at LISA with their own UV platform and will be published in the coming years (Collado et al., in preparation).

## 7.4 Data needs: what's missing and urgent?

(i) **Rocky exoplanets:** data for rocky exoplanets require chemical networks that include many different species, which can be much more complex than what is usually the case for hydrogen-dominated atmospheres. These species include KOH, NaOH, SiO₂, CaOH, FeO,

MgO, MgOH, as outlined in works such as Tennyson & Yurchenko (2017) and Herbort et al. (2020)

(ii) **Reaction rates for various different molecules:** Helling (2021) call for more thermochemical data, particularly reaction rates, for elements other than CHON(S). This has been pointed out by others, for example for phosphine (Bains et al. 2021), which is applicable to Venus as well as exoplanet atmospheres; for chlorine-bearing species which would be helpful for Mars; and species such as TiO and VO for high-temperature exoplanet atmospheres. Other elements which ideally would be included in chemical schemes include Si, Mg, Na, K, Ca, and Al (Fortney et al. 2019).

(iii) **Reaction rates for ions:** ion chemistry may affect the deeper layers of the atmosphere through, for example, the formation of haze. The ionosphere is also relevant because it connects with the upper atmospheres and therefore with the long-term evolution of the planet's composition.

(iv) **Reaction rate coefficients at a variety of pressures and temperatures:** Reaction rates must therefore be known at temperatures ranging from ~ 30 K to above 3000 K, and because the deep atmospheric layers are chemically mixed with the layers probed by spectroscopic observations, at pressures up to about ~ 100 bar.

(v) **Condensation:** some codes, such as GGchem (Woitke et al. 2018) and the latest version of FastChem (Kitzmann et al. 2023), already include condensation. Expanding more codes to explicitly include condensation and including additional elements in chemical networks will be useful to fully characterize where the elements of an atmosphere will go under chemical equilibrium conditions.

(vi) **Gas-particle processes:** when aerosol particles are present in the atmosphere, gas-particle chemistry has been shown to influence not only the particles, but also the atmospheric content (Cavalié et al. 2023; Petera et al. 2023, and Knížek et al., in preparation). For that purpose, sticking coefficients of gas components on solid particles are largely missing.

# 8 UV

## PHOTODISSOCIATION/PHOTOABSORPTION DATA

The need for a description of molecular absorption in the ultraviolet (UV) region is motivated mainly by the following two reasons: (1) direct observations in the UV, which are not in the *Ariel* instrumental range but are of relevance to a number of existing, past, or potential future instruments; the WFC3/UVIS instrument on board the *HST* (200–1000 µm), the *Galaxy Evolution Explorer* (*GALEX*) (135–280 nm) (Viswanath et al. 2020), the Far Ultraviolet Spectroscopic Explorer (FUSE) (90.5–119.5 nm) (Moos, McCandliss & Kruk 2004), Cassini Ultra-violet Imaging Spectrograph (UVIS) (55.8–190 nm) (Esposito et al. 2004), and the Habitable Worlds Observatory (HWO) future mission concept; and (2) utilization in atmospheric chemistry calculations for considering the chemical consequences of photodissociation, see Section 7. For example, improving the cross-section data of H₂O in the vacuum ultraviolet (VUV, ~100–200 nm) leads to significant changes in the outcome of photochemistry: for an abiotic habitable planet with an anoxic, CO₂-N₂ atmosphere orbiting a Sun-like star, it enhances the OH production which in turn suppresses a broad range of species in anoxic atmospheres (Ranjan et al. 2020), including H₂ and CH₄. This is just one mechanism, clearly showing the sensitivity of the models to UV cross-sections and thus highlighting the critical need for laboratory data used photochemical models, whether from measurements or calculations. The impact of the strong variation of CO₂ absorption cross-sections with temperature on abundance and photodissociation
additional sources of stellar spectra available from the MUSCLES survey of 11 low-mass, planet-hosting stars<sup>79</sup> (France et al. 2016). We refer to works such as Gandhi & Madhusudhan (2017) and Shulyak et al. (2020) for further information on the topic of stellar spectra for use in characterising exoplanet atmospheres.

Although the primary focus of the working group who initiated this white paper is for data needs appropriate for the Ariel space mission, we do discuss data for e.g. spectral regions which fall outside the observing window of Ariel. Such regions can be very useful for theoretical models which inform predictions for target planets for the Ariel mission, or for observations which are complementary to those planned by Ariel. Many of these data needs will be shared by all those in the exoplanet atmosphere community, not only those working directly on preparing for the Ariel space mission.

## 10.1 Ariel-data GitHub platform

The description of the data associated with this paper and the Ariel mission in general is available at GitHub via the project https://github.com/Ariel-data. The goal of this platform is to provide a go-to place both for the data-users and data-providers, for the users to highlight their data needs and make requests, and for the data providers to link to the available data. As an open access tool, GitHub provides huge advantages of forming direct dialogues between these sides of the community, where the modelers can make their shopping-lists for the data producers to start planning their calculations or experiments, even for those who are currently not directly involved in the Ariel consortium or in the field of exoplanetary science in general.

We invite everyone to test and make use of this system.

## 11 DATA AVAILABILITY

The data discussed in this paper will be available for access at GitHub via the project https://github.com/Ariel-data, which was created as a platform to bridge the two communities, data-providers and data-users. As an open platform, for the users, it provides an efficient way to communicate their needs, while the data providers can use it to make their work more visible. Any data-related GitHub "issues" raised for the attention of the Ariel Working-Group will be visible to the entire community of the lab data providers, from experimentalists to theoreticians.

## 12 ACKNOWLEDGEMENTS

The ExoMol project is supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme through Advance Grant numbers 883830 (ExoMolHD).

K.L.C was funded by UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee as part of an ERC Starter Grant [grant number EP/Y006313/1].

S.R. acknowledges funding by the Belgian Science Policy Office (BELSPO) through the FED-tWIN program

(Prf-2019-077 - RT-MOLEXO) and through financial and contractual support coordinated by the ESA Prodex Office (PEA 4000137943, 4000128137).

A.F. acknowledges funding by the Knut and Alice Wallenberg Foundation (KAW 2020.0303) and the Swedish Research Council (2020-00238).

This research was supported by the Excellence Cluster ORIGINS which is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC-2094 - 390783311.

A.V.N. and O.E. acknowledge the support from the Russian Scientific Foundation (RSF, No. 22-42-09022).

M.R. acknowledges support from the French ANR TEM-MEX project (Grant 21-CE30-0053-01) and from the Romeo computer center of Reims Champagne-Ardenne.

F. S. acknowledges support from the "Institut National des Sciences de l'Univers" (INSU), the "Centre National de la Recherche Scientifique" (CNRS) and "Centre National d'Etudes Spatiales" (CNES) through the "Programme National de Planétologie".

T. Zi acknowledges NVIDIA Academic Hardware Grant Program for the use of the Titan V GPU card and the support by the CHEOPS ASI-INAF agreement n. 2019-29-HH.0 and the Italian MUR Departments of Excellence grant 2023-2027 "Quantum Frontiers".

Ch. H and H.L.M. acknowledge funding from the European Union H2020-MSCA-ITN-2019 under grant agreement no. 860470 (CHAMELEON).

O.V. acknowledges funding from the ANR project 'EXACT' (ANR-21-CE49-0008-01) and the Centre National d'Études Spatiales (CNES).

JKB is supported by an STFC Ernest Rutherford Fellowship, grant number ST/T004479/1.

A.M. (Alessandra Migliorini) acknowledges funding from the Italian Space Agency (ASI) contract with the National Institute for Astrophysics (INAF) n. 2018-22-HH.0.1-2020.

M.R. acknowledges the support by the DFG priority program SPP 1992 "Exploring the Diversity of Extrasolar Planets" (DFG PR 36 24602/41).

A.K. (Antonín Knížek) acknowledges support from grant no. 24-12656K by the Czech Science Foundation and the ESA Prodex project under PEA 4000129979.

I.E.G and R.J.H acknowledge funding support through NASA grant 80NSSC23K1596 and NASA PDART grant 80NSSC24K0080.

Z.M. acknowledges, through Centro de Química Estrutural, the financial support of Fundação para a Ciência e Tecnologia (FCT) for the projects UIDB/00100/2020 and UIDP/00100/2020, and through Institute of Molecular Sciences, the financial support of FCT for the project LA/P/0056/2020.

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The special but typical feature of molecular spectra in the UV/VUV wavelength region is their continuum. Their featureless character is due to the dissociative effects in the high energy/low wavelength region. It is this type of spectra produced by transitions to or from states above a dissociation limit and caused by the dissociation that we mainly discuss in this section. Photoabsorption includes both bound-bound transitions (these appear as either sharp lines or continuum) and bound-free photodissociation (continuum) transitions, see e.g. Pezzella, Yurchenko & Tennyson (2021) and Tennyson et al. (2023). Bound-bound transitions which result in sharp lines are often included as part of a line list which extends into the UV region (see Section 2). These types of spectra can be completely featureless for transitions to the continuum (from bound states to fully unbound, or dissociative states, e.g. HF above 100–160 nm; Pezzella et al. 2022) or mostly consisting of sharp lines (from bound to bound or quasi-bound states, e.g. MgH in the A-X band at 0.5 µm; Owens et al. 2022a), or anything in between (Pezzella et al. 2022). Both continuum absorption and photodissociation need to be represented as cross-sections rather than the lines of a typical line list, as they are continuum processes.

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<footnote n="74">⁷⁴https://www.anr-exact.cnrs.fr/</footnote>

<footnote n="75">⁷⁵http://astrochemistry.ca.astro.it/database/pahs.html</footnote>
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<table><caption>**Table 15.** Photodissociation thresholds and threshold products in the Leiden data base (Heays et al. 2017; Hrodmarsson & Van Dishoeck 2023).</caption><thead><tr><th>Species</th><th>Photodis. products</th><th>Thresh Photodis</th><th>Species</th><th>Photodis. products</th><th>Thresh Photodis</th><th>Species</th><th>Photodis products</th><th>Thresh Photodis</th></tr></thead><tbody><tr><td>AlH</td><td>Al + H</td><td>392</td><td>CO<sub>2</sub></td><td>CO + O</td><td>227</td><td>NH<sub>2</sub>CHO</td><td>NH<sub>3</sub> + CO</td><td>208</td></tr><tr><td>C<sub>2</sub></td><td>C + C</td><td>193</td><td>CS</td><td>C + S</td><td>168</td><td>NH<sub>3</sub></td><td>NH + H<sub>2</sub></td><td>301</td></tr><tr><td>C<sub>2</sub>H</td><td>C<sub>2</sub> + H</td><td>253</td><td>CS<sub>2</sub></td><td>CS + S</td><td>278</td><td>NO</td><td>N + O</td><td>191</td></tr><tr><td>C<sub>2</sub>H<sub>2</sub></td><td>C<sub>2</sub>H + H</td><td>217</td><td>H<sub>2</sub></td><td>H + H</td><td>274</td><td>NO<sub>2</sub></td><td>NO + O</td><td>398</td></tr><tr><td>C<sub>2</sub>H<sub>3</sub></td><td>H<sub>2</sub>CC + H</td><td>1240</td><td>H<sub>2</sub><sup>+</sup></td><td>H + H<sup>+</sup></td><td>290</td><td>NaCl</td><td>Na + Cl</td><td>293</td></tr><tr><td>C<sub>2</sub>H<sub>4</sub></td><td>C<sub>2</sub>H<sub>2</sub> + H<sub>2</sub></td><td>258</td><td>H<sub>2</sub>CO</td><td>H<sub>2</sub> + CO</td><td>361</td><td>NaH</td><td>Na + H</td><td>606</td></tr><tr><td>C<sub>2</sub>H<sub>5</sub></td><td>C<sub>2</sub>H<sub>4</sub> + H</td><td>260</td><td>H<sub>2</sub>CS</td><td>H<sub>2</sub> + CS</td><td>313</td><td>O<sub>2</sub></td><td>O + O</td><td>242</td></tr><tr><td>C<sub>2</sub>H<sub>5</sub>OH</td><td>C<sub>2</sub>H<sub>5</sub>O + H</td><td>208</td><td>H<sub>2</sub>O</td><td>H + OH</td><td>242</td><td>O<sub>2</sub><sup>+</sup></td><td>O + O<sup>+</sup></td><td>186</td></tr><tr><td>C<sub>2</sub>H<sub>6</sub></td><td>C<sub>2</sub>H<sub>5</sub> + H</td><td>287</td><td>H<sub>2</sub>O<sub>2</sub></td><td>OH + OH</td><td>556</td><td>O<sub>3</sub></td><td>O<sub>2</sub> + O</td><td>1180</td></tr><tr><td>C<sub>3</sub></td><td>C<sub>2</sub> + C</td><td>268</td><td>H<sub>2</sub>S</td><td>H + SH</td><td>318</td><td>OCS</td><td>CO + S</td><td>280</td></tr><tr><td>C<sub>3</sub>H<sub>3</sub></td><td>C<sub>3</sub>H<sub>2</sub> + H</td><td>300</td><td>HC<sub>3</sub>H</td><td>l-C<sub>3</sub>H + H</td><td>400</td><td>OH</td><td>O + H</td><td>279</td></tr><tr><td>C<sub>3</sub>H<sub>7</sub>OH</td><td>C<sub>3</sub>H<sub>7</sub>O + H</td><td>208</td><td>HC<sub>3</sub>N</td><td>H + C<sub>3</sub>N</td><td>244</td><td>OH<sup>+</sup></td><td>O<sup>+</sup> + H</td><td>247</td></tr><tr><td>CH</td><td>C + H</td><td>358</td><td>HCN</td><td>H + CN</td><td>243</td><td>PH</td><td>P + H</td><td>398</td></tr><tr><td>CH<sup>+</sup></td><td>C + H<sup>+</sup></td><td>303</td><td>HCO</td><td>H + CO</td><td>2037</td><td>PH<sup>+</sup></td><td>P<sup>+</sup> + H</td><td>369</td></tr><tr><td>CH<sub>2</sub></td><td>C + H</td><td>417</td><td>HCO<sup>+</sup></td><td>H + CO<sup>+</sup></td><td>145</td><td>S<sub>2</sub></td><td>S + S</td><td>283</td></tr><tr><td>CH<sub>2</sub><sup>+</sup></td><td>CH<sup>+</sup> + H</td><td>204</td><td>HCOOH</td><td>H + HCOO</td><td>333</td><td>SH</td><td>S + H</td><td>345</td></tr><tr><td>CH<sub>3</sub></td><td>CH + H<sub>2</sub></td><td>271</td><td>HCl</td><td>H + Cl</td><td>279</td><td>SH<sup>+</sup></td><td>S<sup>+</sup> + H</td><td>320</td></tr><tr><td>CH<sub>3</sub>CHO</td><td>HCO + CH<sub>3</sub></td><td>342</td><td>HCl<sup>+</sup></td><td>H + Cl<sup>+</sup></td><td>267</td><td>SO</td><td>S + O</td><td>233</td></tr><tr><td>CH<sub>3</sub>OCH<sub>3</sub></td><td>CH<sub>3</sub>O + CH<sub>3</sub></td><td>196</td><td>HF</td><td>H + F</td><td>211.4</td><td>SO<sub>2</sub></td><td>O + SO</td><td>219</td></tr><tr><td>CH<sub>3</sub>OCHO</td><td>HCO + CH<sub>3</sub></td><td>248</td><td>HNC</td><td>H + CN</td><td>165</td><td>SiH</td><td>Si + H</td><td>417</td></tr><tr><td>CH<sub>3</sub>CN</td><td>CH<sub>2</sub>CN + H</td><td>308</td><td>HNCO</td><td>NH + CO</td><td>333</td><td>SiH<sup>+</sup></td><td>Si<sup>+</sup> + H</td><td>391</td></tr><tr><td>CH<sub>3</sub>NH<sub>2</sub></td><td>CH<sub>3</sub>NH + H</td><td>244</td><td>HO<sub>2</sub></td><td>H + O<sub>2</sub></td><td>476</td><td>SiO</td><td>Si + O</td><td>150</td></tr><tr><td>CH<sub>3</sub>OH</td><td>CH<sub>3</sub>O + H</td><td>280</td><td>LiH</td><td>Li + H</td><td>579</td><td>c-C<sub>3</sub>H</td><td>C<sub>3</sub> + H</td><td>289</td></tr><tr><td>CH<sub>3</sub>SH</td><td>CH<sub>3</sub>S + H</td><td>331</td><td>MgH</td><td>Mg + H</td><td>623</td><td>c-C<sub>3</sub>H<sub>2</sub></td><td>c-C<sub>3</sub>H + H</td><td>284</td></tr><tr><td>CH<sub>4</sub></td><td>CH<sub>3</sub> + H</td><td>277</td><td>N<sub>2</sub></td><td>N + N</td><td>127</td><td>SiO</td><td>Si + O</td><td>150</td></tr><tr><td>CH<sub>4</sub><sup>+</sup></td><td>CH<sub>3</sub><sup>+</sup> + H</td><td>1031</td><td>N2O</td><td>N<sub>2</sub> + O</td><td>735</td><td>l-C<sub>3</sub>H</td><td>C<sub>3</sub> + H</td><td>379</td></tr><tr><td>CN</td><td>C + N</td><td>160</td><td>NH</td><td>N + H</td><td>362</td><td>l-C<sub>3</sub>H<sub>2</sub></td><td>l-C<sub>3</sub>H + H</td><td>320</td></tr><tr><td>CO</td><td>C + O</td><td>110</td><td>NH<sup>+</sup></td><td>N<sup>+</sup> + H</td><td>281</td><td>l-C<sub>4</sub></td><td>C<sub>3</sub> + C</td><td>263</td></tr><tr><td>CO<sup>+</sup></td><td>C<sup>+</sup> + O</td><td>149</td><td>NH<sub>2</sub></td><td>NH + H</td><td>314</td><td>l-C<sub>4</sub>H</td><td>C<sub>4</sub> + H</td><td>267</td></tr><tr><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>l-C<sub>5</sub>H</td><td>C<sub>5</sub> + H</td><td>348</td></tr></tbody></table>

<table><caption>**Table 16.** Ionization thresholds in the Leiden data base (Heays et al. 2017; Hrodmarsson & Van Dishoeck 2023).</caption><thead><tr><th>Species</th><th>Thresh Photoion</th><th>Species</th><th>Thresh Photoion</th><th>Species</th><th>Thresh Photoion</th><th>Species</th><th>Thresh Photoion</th></tr></thead><tbody><tr><td>Al</td><td>207</td><td>CH<sub>3</sub>OH</td><td>113</td><td>HCO</td><td>152</td><td>O<sub>2</sub></td><td>103</td></tr><tr><td>AlH</td><td>156</td><td>CH<sub>3</sub>SH</td><td>131</td><td>HCOOH</td><td>110</td><td>O<sub>3</sub></td><td>99</td></tr><tr><td>C</td><td>110</td><td>CH<sub>4</sub></td><td>98</td><td>HCl</td><td>97</td><td>OCS</td><td>111</td></tr><tr><td>C<sub>2</sub></td><td>102</td><td>CN</td><td>87</td><td>HF</td><td>77.5</td><td>OH</td><td>95</td></tr><tr><td>C<sub>2</sub>H</td><td>107</td><td>CO</td><td>88</td><td>HNC</td><td>103</td><td>P</td><td>118</td></tr><tr><td>C<sub>2</sub>H<sup>-</sup></td><td>412</td><td>CO<sub>2</sub></td><td>90</td><td>HNCO</td><td>108</td><td>PH</td><td>122</td></tr><tr><td>C<sub>2</sub>H<sub>2</sub></td><td>109</td><td>CS</td><td>110</td><td>HO<sub>2</sub></td><td>109</td><td>Rb</td><td>297</td></tr><tr><td>C<sub>2</sub>H<sub>3</sub></td><td>147</td><td>CS<sub>2</sub></td><td>123</td><td>K</td><td>286</td><td>S</td><td>120</td></tr><tr><td>C<sub>2</sub>H<sub>4</sub></td><td>118</td><td>Ca</td><td>203</td><td>Li</td><td>230</td><td>S<sub>2</sub></td><td>133</td></tr><tr><td>C<sub>2</sub>H<sub>5</sub></td><td>153</td><td>Ca<sup>+</sup></td><td>104</td><td>LiH</td><td>152</td><td>SH</td><td>119</td></tr><tr><td>C<sub>2</sub>H<sub>5</sub>OH</td><td>119</td><td>Cl</td><td>96</td><td>Mg</td><td>162</td><td>SO</td><td>120</td></tr><tr><td>C<sub>2</sub>H<sub>6</sub></td><td>108</td><td>Co</td><td>158</td><td>Mn</td><td>167</td><td>SO<sub>2</sub></td><td>100</td></tr><tr><td>C<sub>3</sub></td><td>102</td><td>Cr</td><td>183</td><td>N</td><td>85</td><td>Si</td><td>152</td></tr><tr><td>C<sub>3</sub>H<sub>3</sub></td><td>143</td><td>Fe</td><td>158</td><td>N<sub>2</sub></td><td>79</td><td>SiH</td><td>156</td></tr><tr><td>C<sub>3</sub>H<sub>7</sub>OH</td><td>123</td><td>H</td><td>91</td><td>N<sub>2</sub>O</td><td>96</td><td>SiO</td><td>108</td></tr><tr><td>C<sub>2</sub>H<sup>-</sup></td><td>348</td><td>H<sup>-</sup></td><td>1470</td><td>NH</td><td>92</td><td>Ti</td><td>182</td></tr><tr><td>C<sub>6</sub>H<sup>-</sup></td><td>327</td><td>H<sub>2</sub></td><td>82</td><td>NH<sub>2</sub></td><td>111</td><td>Zn</td><td>132</td></tr><tr><td>CH</td><td>117</td><td>H<sub>2</sub>CO</td><td>114</td><td>NH<sub>2</sub>CHO</td><td>121</td><td>c-C<sub>3</sub>H</td><td>129</td></tr><tr><td>CH<sub>2</sub></td><td>119</td><td>H<sub>2</sub>CS</td><td>135</td><td>NH<sub>3</sub></td><td>122</td><td>c-C<sub>3</sub>H<sub>2</sub></td><td>136</td></tr><tr><td>CH<sub>3</sub></td><td>126</td><td>H<sub>2</sub>O</td><td>98</td><td>NO</td><td>134</td><td>l-C<sub>3</sub>H</td><td>136</td></tr><tr><td>CH<sub>3</sub>CHO</td><td>121</td><td>H<sub>2</sub>O<sub>2</sub></td><td>117</td><td>NO<sub>2</sub></td><td>127</td><td>l-C<sub>3</sub>H<sub>2</sub></td><td>119</td></tr><tr><td>CH<sub>3</sub>OCH<sub>3</sub></td><td>127</td><td>H<sub>2</sub>S</td><td>119</td><td>Na</td><td>241</td><td>l-C<sub>4</sub></td><td>116</td></tr><tr><td>CH<sub>3</sub>OCHO</td><td>113</td><td>HC<sub>3</sub>H</td><td>139</td><td>NaH</td><td>179</td><td>l-C<sub>4</sub>H</td><td>129</td></tr><tr><td>CH<sub>3</sub>CN</td><td>102</td><td>HC<sub>3</sub>N</td><td>107</td><td>Ni</td><td>162</td><td>l-C<sub>5</sub>H</td><td>168</td></tr><tr><td>CH<sub>3</sub>NH<sub>2</sub></td><td>135</td><td>HCN</td><td>91</td><td>O</td><td>91</td><td>—</td><td>—</td></tr></tbody></table>
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in preparation). The recent AloHa AlH line list gives an example of the treatment of mixed spectral lines and continuum absorption in UV (Yurchenko et al. 2024c) while Pezzella et al. (2022) gives an example of photodissociation cross-sections.

Absolute VUV branching ratios (sometimes known as quantum yields) of molecules are generally not very well known, in part due to the experimental difficulties of measuring them. Rotationally resolved absolute photodissociation branching ratios of small molecules like CO (Gao et al. 2020b, 2013; Ranjan et al. 2020), CO₂ (Lu et al. 2014), N₂ (Song et al. 2016; Shi et al. 2017), and others have been obtained using two tunable VUV laser systems. Other advancements have been in the use of VUV free electron lasers such as the one in the Dalian Light Source in China, where VUV photodissociation branching ratios have been measured for species such as H₂S (Zhou et al. 2020), CS₂, and OCS along with even more recent results of H₂O (Chang et al. 2023; Wogan et al. 2024). There are more recent measurements in the context of exoplanet atmospheres that are noteworthy such as recent measurements of ethane (C₂H₆) (Chang et al. 2020) considering its relevance to both Hycean atmospheres and Enceladus. As the sizes of molecules grow, the fragmentation patterns become more difficult to disentangle. In the case of C₂H₆, there are seventeen different fragmentation channels available at the Lyα limit, each other with different sets of fragmentation products which can be formed in various electronically excited states.

Regarding the electronically excited species, to a zeroth order approximation the electronic fluorescence proceeds faster than the collision frequency in the atmosphere in question. The validity of this approximation is dependent on the pressure and temperature of the relevant environments but typically non-dissociative fluorescence lifetimes of small molecules are of the order of one to a few microseconds. For environments where collision rates and these fluorescence rates are comparable, collisions with electronically excited species should not be neglected because they could lead to different photochemical paradigms that are currently neglected in most atmospheric models. For example, when H₂O is photodissociated from the D state, OH is formed in the electronically excited A state which has a fluorescent lifetime of 1 ms (Livingston Large & Kliewer 2024).

## 8.2 What's being worked on?

(i) ExoMol is working on producing temperature-dependent UV photoabsorption and photodissociation data for a variety of molecules including OH, SO, and HCN.

(ii) Recently, acetylene has been measured (Fleury et al. 2023a) and more data are to be published soon (Fleury et al., in preparation), as part of the EXACT project,<fnref n="76" /> where UV absorption cross-sections are measured experimentally. The EXACT project aims to measure UV absorption cross-sections at high temperatures for many species, using their experimental setup at LISA.

(iii) VUV photoabsorption cross-sections and VUV photoionization cross-sections are currently being studied for a number of molecules at the DESIRS beamline at the SOLEIL synchrotron facility in France. Current efforts involve sulphur-containing species; photoabsorption cross-section of CS and C₂ will be measured and the photoionization cross-sections of sulphur containing radicals such as CCS will be investigated.

(iv) PAHs: similar to the recent experimental work of Chowdhury et al. (2024) on the cyanonaphthalenes, the photoionization of a large sample of neutral PAHs of varying sizes and symmetries are being investigated (Hrodmarsson et al., in preparation) to improve previous experimental data on PAH photoabsorption data from the 1990's (Verstraete et al. 1990; Tobita et al. 1994; Jochims, Baumgaertel & Leach 1996; Jochims et al. 1997; Jochims, Baumgärtel & Leach 1999) currently in use to describe the contribution of neutral PAHs to the gas heating in the ISM. At the LISA laboratory, UV absorption cross-sections for several molecules of atmospheric importance are currently under study. A future instrumental direction at LISA is to construct a double VUV laser system with a VMI spectrometer that will be dedicated to measuring VUV branching ratios of molecules of interest to exoplanet atmospheres.

## 8.3 Data needs: what's missing and urgent?

(i) **Photoabsorption cross-sections at high temperatures:** Fortney et al. (2019) (a collaborative Astro2020 Science White Paper) expressed the need for photoabsorption cross-sections measurements and their temperature dependence in the VUV wavelength range (115–230 nm) for molecules such as N₂, O₂, O₃, H₂O, CO, CO₂, CH₄, NH₃, TiO, VO, HCN, C₂H₂, H₂S, PH₃.

(ii) **Branching ratios:** Despite a huge progress in laboratory provision of branching ratios, e.g. Burkholder et al. (2020), more data for atmospheric studies is needed (Jenkin et al. 2019; Gao et al. 2020b).

(iii) **Non-LTE:** most photodissociation cross sections come from the study of molecules in thermal equilibrium. However, there it is generally accepted that the upper atmospheres of most observable exoplanets are not thermal and so Non-LTE cross-sections will be needed.

# 9 DATA STANDARDS

## 9.1 Principles of and need for data standards

Standards for the description of atomic and molecular data are crucial for their effective use in the study of exoplanet atmospheres. The successful adoption of a set of standards for the description of species, quantum states, and spectroscopic processes facilitates interoperability between simulation codes, data exchange between researchers, the long-term preservation of data, and automatic processing of data, for example through application programming interfaces (APIs).

In any discussion about data standards a distinction must be made between metadata and data formats. All data sets are associated, implicitly or explicitly, with some set of conventions for describing metadata: the context, applicability, provenance, physical units, and additional information which give meaning to the raw numbers of the data themselves. The format in which these numerical data are provided may vary between databases (depending, for example, on the size of the data sets). However, an interoperable ecosystem of data bases must adopt standardized, unambiguous, and machine-readable metadata conventions to allow comparison and aggregation across data sets.

Modern data base services aspire to conform to so-called FAIR principles of data curation (Wilkinson et al. 2016); in brief, FAIR data are:

(i) Findable: associated with globally unique and persistent identifiers; registered in a searchable, online resource.

(ii) Accessible: retrievable through a standardized protocol.

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