dataesr/scientific-paragraphs-categorization huggingface dataset:
Model validation In this section, we validate the FENNIX-OP1 model using a few examples of applications. First, we compute bond dissociation energy proles of typical small molecules and show that the model is able to consistently break covalent bonds. Then, we show that the model is able to produce stable and accurate MD simulations of condensed-phase water including nuclear quantum effects. For this study, we included nuclear quantum effects using the adaptive quantum thermal bath (adQTB) method introduced in ref. 53. We showed in previous studies  54  that the adQTB provides an efficient and accurate alternative to path integralsthe gold standard method for including NQEs in MD simulationsat a cost similar to classical MD. For these two examples, we compare our results to the ANI models  16  that have a comparable scope as FENNIX-OP1 in terms of chemical diversity and were trained on similar datasets (note that we used the ANI models as provided in the torchANI package and did not re-train them).
Finally, we show that FENNIX-OP1 is able to produce stable dynamics of organic molecules solvated in water and provides a good qualitative description of the torsional free energy landscape of the alanine dipeptide in solution. All calculations for this work were performed on an Nvidia A100 GPU using our custom TorchNFF package that is built on top of Pytorch 70 and provides the implementation for FENNIX as well as a simple MD algorithm for NVT simulations in periodic boundary conditions (with Ewald summation for electrostatic interactions) and an efficient Pytorch implementation of the adQTB. TorchNFF is in an early development version and is thus not yet optimized. For example, a FENNIX-OP1 simulations of a box of 216 molecules of water in periodic boundary conditions can currently reach about 0.
Fig. 3  shows the potential energy curves for (a) the dissociation of a hydrogen atom in methane; (b) the asymmetric   75  that was tted on CCSD(T) data and multireference CI/aug-cc-pV6Z from ref. 76 for the H-F molecule) and the ANI models. We see that FENNIX-OP1 consistently captures a smooth dissociation curve thanks to the physicallymotivated choice of reference energy. On the other hand, the ANI models, for which the reference energy was set according to average values over the dataset, fail to reproduce the dissociation curves for large distances. FENNIX-OP1 agrees particularly well with the reference for water as the Partridge-Schwenke PES was included in the training set. For the other two molecules, it tends to underestimate the dissociation barrier. Interestingly, while the training data did not contain covalent interaction energies for F, the model is still able to reasonably generalize its prediction.
In order to produce quantitative results and stable dynamics in this context, next iterations of the FENNIX model will need to explicitly handle charged systems, which will require a more thorough dataset. The recently introduced SPICE dataset 97 could be a good complement to the ones already used in this work as it provides reference data for many charged molecules and focuses on biological systems (however at the lower quality DFT level). Furthermore, it will require ner description of molecular interactions through the inclusion of more advanced energy terms (such as multipolar electrostatics and polarization).
Conclusion and outlooks We introduced FENNIX, a general class of models representing molecular interactions using an hybrid ML/FF approach. It builds upon the latest equivariant architectures to construct an embedding of the local chemical environment which is fed into a multi-output module predicting atom-in-molecule properties. The latter allows for a exible design of potential energy terms. We apply this strategy to design a specic model: FENNIX-OP1, capturing both short-range interactions, with a dedicated ML energy term, and long-range ones using predicted atomic volumes and charges that parametrize both a charge penetration corrected electrostatic energy and a Tkatchenko-Scheffler like dispersion one. FENNIX-OP1 is rst trained on both the ANI-1ccx and the DES370K datasets which contain monomers, dimers and a few multimeric structures focusing on small neutral organic molecules.
It is then rened for water using the q-AQUA dimers dataset and the highly accurate Partridge-Schwenke potential energy surface for a nal RMSE of 0. 35 kcal mol -1 on interaction energies. We then showed that the model is stable during molecular dynamics (including NQEs via adQTB) and able to generalize to condensed phase (despite the lack of reference data), capturing structural properties of bulk water and solvated organic molecules. More precisely, it qualitatively reproduces the torsional free energy prole of the solvated alanine dipeptide using enhanced sampling techniques and yields stable trajectories of the 1FSV protein in gas phase. Interestingly, we showed that the model learned some generic concepts (such as dissociation proles) independently of the chemical species, which are then specically rened for each elementwith some transferability across the periodic tablethanks to the continuous encoding of their positions in the periodic table.
Indeed, specialized models  19, 20, 50  can achieve very good accuracy on multiple properties of the system they target, that general ones (such as FENNIX-OP1) currently do not match. On the other hand, the exibility of general models enables the simulation of more complex systems involving a large variety of constituents, as is oen the case in biochemistry. A promising strategy that could provide a solid middle-ground is the ne-tuning approach where a general model is slightly re-trained with limited data to better represent a specic target system. Further work will focus on the development and use of more general datasets (such as the SPICE dataset  97  ), the extension of the FENNIX approach and its inclusion in the optimized Deep-HP platform  98, 99  present within the Tinker-HP package.
Acknowledgements This work was made possible thanks to funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement No. 810367 ), project EMC2. Computations have been performed at  GENCI (IDRIS, Orsay, France  and  TGCC ,  Bruyères le Chatel ) on grant no  A0130712052.
Assessment of past anthropogenic modifications of land-cover dynamics is key to understanding the human role in the Earth system. Recent advances in palaeoenvironmental sciences allow us to assess the long-term impacts of anthropization on ecosystems, landscapes, and land-cover. Our study aims to evaluate the role of human impact on European land-cover over the past 6000 years by comparing two independent datasets. First, we use a dynamic vegetation model forced by debiased climate model outputs. The climate model uses natural forcings only and therefore the computed vegetation distribution is interpreted as the potential natural vegetation. Second, we use pollen-based reconstructions, which intrinsically include anthropogenic influence. The discrepancies between the two datasets are attributed to human activity and quantified in a form of a human pressure index (HPI).
, 2020; Goldewijk et al. , 2017a; Kaplan et al. , 2011; Gronenborn and Horejs, 2021)  as well as by a substantially changing climate  (Arthur et al. , 2022; Zhang et al. , 2017). Moreover, analysis of the European Pollen Database indicated that anthropogenic land-cover change in pollen records becomes detectable from 6000 BP  (Fyfe et al. , 2015). Hence, in this study we focus on quantifying the anthropogenic impact on European land-cover over the period of the second half of the Holocene (6200 CE). Estimation of the magnitude of human-induced changes in landcover is often performed through analysis of palaeoecological data acquired from pollen-based reconstructions  (Bartlein et al. , 2011; Dallmeyer et al. , 2019; Marquer et al. , 2017; Prentice et al. , 1998; Strandberg et al. , 2022). One of such reconstructions, the Regional Estimates of VEgetation Abundance from Large Sites (REVEALS) model  (Sugita, 2007a) , was used to create gridded pollen-based estimates of Holocene plant cover for NW-central Europe north of the Alps (five time windows of the Holocene)  (Trondman et al.
However, it is far from being sufficient to reflect vegetation changes at the level of complexity needed for our main intercomparison with the REVEALS output, as described further in this study. This is why the resulting climatic evolution was then bias-corrected and used as an input to a much more complex vegetation model which has the necessary complexity to see the fine-scale structures we are concentrating on. The input was provided as climatologies (climatological means) of daily values for each time window (TW) (Supplementary Table  1 ) without transient change in climate input (equilibrium simulations). To summarise, the use of VECODE in the initial computation affect ed the large-scale climate providing the first-order response of dynamical vegetation coupled to climate at large scales, such as desertification (complete change from trees to grass or bare ground, and vice versa) or large shifts from forests to grassland.
, 2023). A full procedure of the bias correction methodology and its limitations can be found in  Zapolska et al. (2023). The observational reference dataset used for bias correction is the EWEMBI dataset (EartH2Observe, WATCH Forcing Data (WFDEI), and ERA-Interim reanalysis data merged and bias-corrected for the Inter-Sectoral Impact Model Intercomparison Project)  (Lange, 2016). The EWEMBI data covers 38 years from 1979 to 2016. To match to the length of the observation dataset (which is required for the CDF-t methodology), we extracted 38 median years of 14 TWs (Supplementary Table  1 ), consistent with the TWs of the REVEALS pollen-based land-cover reconstruction dataset for further analysis. Following the bias correction, each of 14 sets of climatic parameters was averaged to get daily mean climate characteristics of TWs.
Modelled potential natural vegetation Potential natural vegetation was simulated using the CARbon Assimilation In the Biosphere (CARAIB) model  (Otto et al. , 2002; Laurent et al. , 2008; Warnant et al. , 1994; François et al. , 2011; Dury et al. , 2011)  forced with 14 sets of climates simulated by the iLOVECLIM model (bias-corrected and averaged over TWs, as described above). The CARAIB model is composed of five modules describing respectively (1) the hydrological budget, (2) canopy photosynthesis and stomatal regulation, (3) carbon allocation and plant growth, (4) heterotrophic respiration and litter/soil carbon dynamics, and (5) plant competition and biogeography  (François et al. , 2011). In this study, we did not include the fire module in the simulations, and the fire regime was calibrated based on modern times.
(2002) ,  Laurent et al. (2008) ,  Warnant et al. (1994) ,  François et al. (2011) ,  Dury et al. (2011). The input climatic fields used to run the CARAIB vegetation model were daily values of: (1) the mean near-surface air temperature, (2) the daily amplitude of air temperature change, (3) precipitation, (4) air relative humidity, (5) percentage of sunshine hours and (6) wind speed. In the current study, daily temperature amplitudes and wind speed were taken from observations (Daily temperature amplitudes: Climatic Research Unit (CRU) data, mean over 1901-2015; wind speed: EWEMBI dataset) and kept constant for all time periods. Surface temperature, precipitation, relative humidity, and sunshine hours were derived from iLOVECLIM. Biases in temperature, precipitation and relative humidity were corrected using the CDF-t approach  (Vrac et al.
The REVEALS pollen-based land-cover reconstructions For comparison with the modelled PNV, we used a part of the last set of gridded pollen-based REVEALS plant-cover estimates for 31 taxa at a spatial scale of 1 • × 1 • across 30 • -71 • N, 20 • W-47 • E (north-western, central Europe, Mediterranean area, and part of the East until 47 • E) for 14 contiguous time slices of 100-500 years covering second half of the Holocene  (Serge et al. , 2023). Previous grid-based estimates  (Githumbi et al. , 2022; Trondman et al. , 2015)  were designed for the purpose of conducting studies on the impact of land cover on climate using climate models and DGVMs  (Strandberg et al. , 2022). The REVEALS dataset is the only current land-cover reconstruction approach based on pollen data to quantify past regional cover of individual plant taxa.
It effectively accounts for intertaxonomic differences in pollen productivity and dispersal properties as well as the size and type of sedimentary basins. It combined 1607 pollen records and benefited from earlier efforts and projects (Landclim I and II) in collecting pollen dataset  (Githumbi et al. , 2022; Trondman et al. , 2015)  obtained from databases and individual data contributors. There are many areas of Europe where environments that preserve pollen (i. e. lakes, bogs, forest hollows) are sparse, therefore the geographic distribution of pollen-based reconstruction is uneven. REVEALS transforms pollen assemblages in large lakes to abundance of individual plant taxa in the surrounding vegetation at a large spatial scale (ca. 100 km × 100 km; Hellman et al. , 2008a, b) using a mechanistic modelling approach that uses an empirical understanding of relative pollen production between pollen morphological types, and pollen dispersal mechanisms.
, 2023). The precision of gridded REVEALS estimates is indicated by their standard errors. Caution should be applied when using REVEALS estimates from unreliable grid cells, and when standard errors of the gridded REVEALS estimates are equal or greater than RE-VEALS estimates  (Serge et al. , 2023). Hence, the number of available REVEALS grid cells for analysis varies depending on the availability of reliable pollen data for each TW. Across the 14 TWs studied here, the number of grid cells ranged from 456 to 363. This range reflects variations in quantity and quality of the pollen data available for analysis during each time window.
Classification of plant functional types To reconstruct vegetation patterns and their changes throughout the Holocene we applied the concept of plant functional types (PFTs). Plants were classified into PFTs based on their physical, phylogenetic and phenological characteristics, as well as their bioclimatic and functional features. PFTs included in the CARAIB model are described in  François et al. (1998). In this study, each selected plant taxon was assigned to a specific unique PFT (Table  1 ) based on previously published classification used for model-data comparison in palaeovegetation studies  (François et al. , 2011; Henrot et al. , 2017; Popova et al. , 2013). Standard errors of the REVEALS dataset  (Serge, 2023)  were calculated for each PFT according to the delta method  (Stuart and Ord, 1994).
In CARAIB, plant growth in northern Europe is hindered by frigid climatic conditions of polar tundra, which prevents vegetation from populating the grid cells. Therefore, high latitudes in CARAIB are composed of bare ground with a few herbs (Supplementary Fig. 7 ). Pollen-based reconstructions, however, reflected presence of Betula, Ericaceae, Cyperaceae, Pinus and Poaceae in those areas across all studied TWs. A recent study of  Sun et al. (2022)  demonstrated the impact of the lack of representation of bare ground in REVEALS-based estimates. Hence, to eliminate some of the potential biases related to this issue, we chose to exclude high latitudes (70-71 • N) from the intercomparison. With this we aimed to prevent the negative bias due to low agreement between the studied datasets over these zones, dictated by their technical characteristics.
Comparison between REVEALS estimates and CARAIB simulations Comparison of the CARAIB simulations with the REVEALS estimates required a transformation of the spatial and temporal resolution of the CARAIB model output. We aggregated the PNV simulated by CARAIB to the same grid resolution as the REVEALS estimates (1 • x1 • grid cells) and extracted only those grid cells where the pollen data were available (ranging from 456 to 363 grid cells for the analysed TWs). CARAIB vegetation is represented by two vertical layers that were adjusted to match a resolution of 1 • , used by the REVEALS method. Then, relationship between REVEALS and CARAIB was analysed by computing a weighted matching ratio between PFT fractions of both models, using the following formulas: Similarity between CARAIB and REVEALS estimates per PFT was expressed in matching ratio (MR) values.
Considering that the two datasets are independent and of a different nature, the use of absolute values of MRs for the intercomparison could have been challenging due to limitations and biases of the methodology. Such biases remain in all TWs (assuming their stationarity), but MRs may not be constant through time. In this study it is assumed that this variation occurs due to human impact, which is the factor that is not stationary in time throughout our study period. Therefore, we focused on temporal changes within PFTs rather than absolute differences between the datasets to minimize the impact of such biases on our results. To this end, we have chosen to analyse the relative change in the MR values between each TW and the TW calculated in the most recent period (TW1). MR PFT = frac PFT, WMR was calculated to ensure that more weights are given to the PFTs that are more abundant within a grid cell.
Under an assumption that WMR represents percentage of vegetation unaltered by anthropogenic activity, by reversing it we calculate a human pressure index (HPI): HPI = (100 -WMR) ÷ 100. Then, a mean HPI over each 1 • -wide latitudinal zone was calculated for each TW. Translating the discrepancies between the two studied datasets into the HPI was made under the assumption that changes in relationship between the two datasets at the second half of the Holocene are mainly caused by anthropogenic activity. This assumption is supported by a growing body of evidence, including palaeoenvironmental reconstructions, that suggests a significant and widespread impact of human activities on European land-cover during this period  (Roberts et al. , 2018 (Roberts et al. , , 2019;; Ruddiman and Ellis, 2009; Nielsen et al.
, 2012; Kaplan et al. , 2009; Ellis et al. , 2013; Strandberg et al. , 2022). While megafauna presence is also not simulated by PNV models, the extinction of many megafaunal species during the late Pleistocene and the Holocene in Europe  (Mann et al. , 2019; Sandom et al. , 2014; Stewart et al. , 2021; Koch and Barnosky, 2006)  decreases their potential role in large-scale changes in vegetation patterns over the second half of the Holocene. Taken together, these lines of evidence support our assumption that anthropogenic activity is the main driver of changes in the relationship between the datasets during the second half of the Holocene.
Correlation with ALCC scenarios In order to compare our results with existing literature on anthropogenic impact on European land-cover, we used two most commonly used in landscape research ALCC scenarios, KK10 and HYDE 3. 2. ALCCs offer the advantage over other datasets as they provide datasets that are continuous in space and time, whilst other approaches (for example, demographic proxies such as archaeological dates (e. g. Shennan et al. , 2013) , or human impact indicators from pollen data) have discontinuous coverage, and are not necessarily linearly related to land cover changes  (Hiscock and Attenbrow, 2016). KK10 and HYDE 3. 2 differ in the way they estimate and represent land-cover change. KK10 consists of data expressed in a form of a deforestation index at annual resolution, which, in this study, was averaged over the years within each of the REVEALS time windows (Supplementary Table  1 ).
HYDE 3. 2 estimates are presented in a form of simulated land use. In this study, to represent anthropogenic land use for agricultural purposes we analysed HYDE 3. 2 data on croplands and grazing. HYDE 3. 2 data is irregularly spaced in time over the Holocene: from 100 years temporal resolution in the Late Holocene to 1000 years in the early and Mid-Holocene. We used linear interpolation to obtain the data for the studied time windows. We also performed transformations of spatial resolution of both ALCC scenarios by aggregating the grid cells to the 1 • x1 • REVEALS grid, and extracted data only for the grid cells that have available data in REVEALS. For comparison between HPI and KK10 and HYDE, we assumed that extracted land use from the ALCC scenarios should be roughly equivalent to human impact on land-cover.
We then analysed correlation between these three datasets to investigate if they represent humaninduced land-cover changes in a similar manner. Additionally, we evaluated the relationship between HPI values and HYDE 3. 2 population density estimates to understand if presence of humans spatially correlates with changes in HPI. HYDE 3. 2 population density is primarily based on demographers' estimates for the last 3000 years  (McEvedy and Jones, 1978) , with earlier population sizes being estimated using back extrapolation  (Goldewijk et al. , 2010).
Results We have analysed palaeovegetation data expressed in fractions of PFTs for 14 TWs throughout the second half of the Holocene (6200 CE) from CARAIB simulations (representing PNV) and REVEALS estimates (representing pollen-based regional vegetation abundance). The definitions of TWs are summarised in Supplementary Table  1. The analysis of the relationship between simulated PNV and pollenbased regional estimates of vegetation abundance is based on the evolution of discrepancies in the two datasets and expressed as relative change in the MR values between each TW and the TW calculated in the most recent period (TW1) (Fig. 1 ). Throughout the studied time period, we observe decreases in MR of herbs (H), broadleaved summergreen boreal/temperate cold trees (BSBTT) and broadleaved summergreen temperate cool trees (BSTCT).
During the whole studied period high HPI is observed in northern Europe (Scandinavia), which is an artefact of intrinsic biases of two datasets compared (Section 4. 4). Hence, the high latitudal zones (70-71 • N) are not included in the subsequent analyses. In Fig. 3 , we subdivide our study area into latitudinal zones of 1 • , corresponding to the size of grid cells used for our analysis. This division allows us to display latitudinal trends of HPI changes through the second half of the Holocene in Europe. We chose to present the dynamic per latitudinal zone based on the historical context of our study period in Europe, which corresponds to the transition and establishment of agriculture  (Stephens et al. , 2019; Morrison et al. , 2021; Harrison et al. , 2020; Goldewijk et al. , 2017a; Kaplan et al.
3  and 4 ). To further investigate the relationship between HPI and agriculture, we analyse the onset of agriculture in REVEALS vegetation reconstructions, marked by first appearance of Cerealia t. And Secale cereale pollen in the dataset. We assume that the agricultural onset is represented as a year of the earliest (within the time bounds of our study) appearance of the aforementioned agricultural taxa in REVEALS at each grid cell (Supplementary Fig. 6 ). However, the identification of the onset of agriculture based on agricultural PFT in REVEALS is constrained by the limitations of this approach, described by Trondman   et al. (2015). We, therefore, do not identify patterns similar to the onset of agriculture in Europe described by the existing anthropogenic land-cover change (ALCC) scenarios.