dataesr/scientific-paragraphs-categorization huggingface dataset:
The SDM is a relatively simple approach that describes transitions among coupled electron-nuclear quantum states in the adiabatic approximation, allowing the generation of greater than 100 datapoints to train the model, while ERT provides an ML model that is robust against overfitting of small datasets, and has been applied to materials data previously. 41  We extract 128 materials from the MC2D database  42  and study the zero-point renormalization (ZPR) of the band gap computed within density functional theory (DFT) as a measure of EPH interactions. In order to understand the relationship between physical observables and ZPR, we build an ERT model fit with nine features that are motivated by physical observables, and analyze the most important features using a SHAPley analysis,  43  a model explainability technique that quantifies how individual features of each material impact the ZPR.
31  We also expect that the ionicity of bonds will impact the polar EPH coupling and to also contribute to the dielectric constant. While the EPH coupling strength is expected to scale with these physical parameters, as the material complexity increases, the interplay between these parameters is not clear; e. g. , the case of compound semiconductors. In addition, this physical intuition does not provide quantitative trends, motivating the following data driven approach. Figure  1  displays the correlation between three physical parameters and ZPR for the full dataset of materials studied. We find a linear relationship between the ZPR and equilibrium structure gaps with a slope of 0. 06 (1a), indicating that the gap is on-average reduced by 6% due to the presence of phonons. Within the Penn model, the average band gap (⟨E g ⟩) and the static dielectric constant (ϵ) share an inverse relationship  55, 56  ; thus, we may expect this correlation to the gap will lead to an inverse relationship to ZPR as is shown in Figure  1b.
We utilize the ERT ensemble model. This ensemble of  decision trees is constructed by training individual trees across the entire train data set, but each point at which a given feature is split is decided randomly instead of per an optimization algorithm. 58. However, by their nature using a single tree is susceptible to overfitting,  58  where small changes in the input data can produce significantly different trees and thus models that do not generalize well to different sets of input data, which can be alleviated by use of ERT. This is particularly important for small datasets as we have here. Indeed, when we at-tempted to use a similar spread of methods, we found ERT ensemble models performed best in our use case as well, producing predictive models with a lower ratio between test and train MSE indicative of lower over-fitting while maintaining acceptable predictive power.
We consider only E g , N , M , ∆ χ , and M and refit our model with only the gap calculated from DFT. As for the full model, we use cross-validation to select optimal parameters, and validate the model on an 80-20 train-test split. The refit model applied to 113 materials results in slightly higher RMSE (train: 53 meV; test: 45 meV) but a reduction in overfitting (Test/Train ratio ∼0. 7). We then applied these optimally selected parameters and retrained our model with the original data set plus 15 additional highgap materials, taken from the C2dB database,  62, 63  to address the model's deficiencies in the very high (¿0. 5) ZPR range. Finally, we applied this model to predict the ZPR for ∼1302 2D materials from C2dB (see SM and supporting documents). We test the ML prediction by comparison to DFT/SDM-predicted ZPR for five randomly selected materials: CaCl 2 ,Ge 2 O 2 , InGaS 2 , SbSeBr, and ZrSSe.
As shown in Table  S3  and Figure  3b , the ML predicts low ZPR values in excellent agreement with PBE. For the more strongly renormalized materials, CaCl 2 and Ge 2 O 2 , the ML model underestimates ZPR because of the sparsity of data at those higher values, leading to a less accurate description. Importantly, the trends among the five materials are well-predicted by the ML, indicating that our model provides a good estimate of different renormalization regimes for little computational cost just by knowing simple physical parameters. In summary, we presented a high-throughput study of the band gap ZPR within 2D materials, utilizing the special displacement method to calculate ZPR at low computational cost. We trained an ERT model on physically motivated properties of the materials in the database in order to analyze the relative importance of different physical observables for the prediction of ZPR and found that the bandgap, in-plane dielectric constant, degree of ionicity, and average mass of atoms are the most significant features.
Additionally, we trained a reduced-feature ERT model, trained only on features that require either no calculation or an inexpensive DFT calculation on a unit cell. This is important because the model can be applied to large databases of materials in order differentiate regimes for renormalization for high-throughput screening, something that would not be possible from first-principles. This enables the identification of materials demonstrating exceptionally strong EPH couplings, facilitating, for example, predictions of potential manifestations of polaronic effects  6. In future work, it will be interesting to explore how the phonon-induced band gap renormalization is affected by a more accurate treatment of electron correlation effects, beyond DFT-PBE. Furthermore, the inclusion of nonadiabatic effects  14, 51  , longrange Fröhlich couplings 20 , tadpole self-energy corrections  53, 54  , and anharmonicity  [64] [65] [66] [67] [68]  which are more complex terms to be amenable to high-throughput machine learning approaches, holds promise as topics of future research.
ACKNOWLEDGEMENTS We acknowledge financial support from the  U. S. Department of Energy (DOE), Office of Science, Basic Energy Sciences  under Award No. DE-SC0023402. We would like to acknowledge computational resources from the Shared Computing Cluster which is administered by  Boston University's Research Computing located  at the  Massachusetts Green High Performing Center (MGHPC)  and the  National Energy Research Scientific Computing Center (NERSC) , a  DOE Office of Science User Facility  supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231 , and HPC resources from the  Texas Advanced Computing Center  (TACC) at The University of Texas at Austin [http://www. tacc. utexas. edu]. F. G. is supported by the  Computational Materials Sciences Program  funded by the  U.
This work received financial support from the  Agence Nationale de la Recherche  ( French National Agency for Research) , https://anr. fr/,( ANR  project  HIGH-TUNE   ANR-16-CE01-0010 ,  ANR  project  MC2 ANR-21-CE46-0013  and  ANR  project  MCG-RAD   ANR-18-CE46-0012 ), from  Region Occitanie  (Projet CLE EDSTAR), from  French government research program  " Investissements d'Avenir " through the  IDEX-ISITE initiative   16-IDEX-0001  ( CAP [20][21][22][23][24][25 ], the  IMobS3 Laboratory of Excellence  ( ANR-10-LABX-16-01 ) and the  SOLSTICE laboratory of Excellence  ( ANR-10-LABX-22-01 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Ruddlesden -Popper and reduced Ruddlesden-Popper nickelates are intriguing candidates for mimicking the properties of high-temperature superconducting cuprates. the degree of similarity between these nickelates and cuprates has been the subject of considerable debate. Resonant inelastic x-ray scattering (RIXS) has played an important role in exploring their electronic and magnetic excitations, but these efforts have been stymied by inconsistencies between different samples and the lack of publicly available data for detailed comparison. To address this issue, we present open RIXS data on La 4 Ni 3 O 10 and La 4 Ni 3 O 8.
However, much of this debate has been complicated by two major difficulties. The first is inconsistent spectra from nominally equivalent or very similar samples, an issue raised specifically in refs. 18, 24. The second is the lack of open data, which precludes detailed direct comparison between samples. Herein, we provide RIXS data from bulk crystals of La 4 Ni 3 O 10 (n = 3) and La 4 Ni 3 O 8 (n = 3, after reduction). In addition, little publicly accessible advice is currently available for comparing RIXS datasets, so we take this opportunity to outline considerations in comparing RIXS data taken under different experimental conditions.
Ni L 3,2 RIXS as well as O K and Ni L 3,2 x-ray absorption spectroscopy (XAS) measurements were performed at the AERHA instrument of the SEXTANTS beamline at the SOLEIL synchrotron. The spectrometer operates by dispersing the x-ray photons as a function of their energy onto a two-dimensional detector via the detailed optical scheme described in ref. 26. A sketch of the experimental geometry is displayed in Fig. 1c. All measurements were performed with the a and c sample axes in the horizontal scattering plane. At the Ni L edges, data were taken at θ i = 15° degrees (θ i is the angle between the incoming x-ray and the sample surface), the linear dichroism is obtained by switching the incident x-ray polarization between π and σ. For the O K edge, a fixed π x-ray polarization was used, the dichroism is obtained by measuring at θ i = 15° and 90°.
RIXS was measured at 2Θ = 95°, with an overall full-width at half-maximum (FWHM) energy resolution of 262 ± 9 meV, chosen in order to balance throughput and resolution. As is standard in modern RIXS experiments, the two-dimensional data are binned in the isoenergetic direction to form spectra and the pixel to energy loss conversion is performed by measuring the elastic line of the spectrometer while changing the beamline energy. The data describing this calibration are provided in the Technical Validation section.
Data Records This Data Descriptor is based on data deposited in the Zenodo general-purpose open repository  27. RIXS data are provided as text files with filename of the form La4Ni3O10_pi_850. 0eV. dat, which specify the material, incident x-ray polarization, and incident energy. This information as well as the sample temperature are provided in the file header. XAS data are also provided as text files with a similar nomenclature, but which includes the absorption edge measured, for instance La4Ni3O8_sigma_OKedgeXAS. dat. These data are also plotted in Figs. 2 3 4. The XAS data displayed in Fig. 5  were taken in total fluorescence yield (TFY), but files also contain data taken in total electron yield (TEY) concomitantly. Finally, the data shown in Fig. 6a ,b are stored in files elastic_850eV.
dat and pixel_energy_dispersion. dat, respectively. The repository illustrates the data content using plotting scripts based on standard python stack of numpy, matplotlib, scipy, and pandas  [28] [29] [30]. The jupyter-repo2docker is used to define the computational environment and making the code executable on services such as mybinder. org. Gaussian peak to a spectrum collected at an energy away from the resonance [Fig. 6a ]. The data collected by the CCD area detector was converted to energy space using the calibration shown in Fig. 6b. Since the La M 4 -and Ni L 3 -edges overlap in energy, we also provide data at the Ni L 2 -edge in Fig. 4.
Usage Notes Several technical issues make it quite difficult to measure RIXS in absolute units. We therefore present data in arbitrary units. We suggest comparing the preset spectra to other works after dividing both spectra by the integrated spectral intensity in the 0. 5-15 eV energy range. It is better to exclude the elastic line from such comparisons, as the quasielastic intensity is particularly sensitive to extrinsic issues such as sample surface flatness. To compare the data presented here with other data, care should be taken to account for possible differences in energy resolution between the different datasets. We suggest comparing data by convolving the higher-resolution spectrum with a lineshape that gives both spectra the same effective resolution.
acknowledgements  Work at Brookhaven  and the  University of Tennessee  (RIXS measurements and the interpretation) was supported by the  U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences , under Award Number  DE-SC0022311. Work at  Argonne  was supported by the  U. S. DOE, Office of Science, Basic Energy Sciences, Materials Science and Engineering Division  (nickelate sample synthesis and RIXS theory). We acknowledge SOLEIL for provision of synchrotron radiation facilities. This research used resources of the  Advanced Photon Source , a  U. S. Department of Energy (DOE) Office of Science user facility at Argonne National Laboratory  and is based on research supported by the  U. S. DOE Office of Science-Basic Energy Sciences , under Contract No. DE-AC02-06CH11357.
This research used resources at the SIX beamline of the National Synchrotron Light Source II, a  U. S. DOE Office of Science User Facility  operated for the  DOE Office of Science by Brookhaven National Laboratory  under Contract No. DE-SC0012704. G. S. C. acknowledges the support of the  Agence Nationale de la Recherche (ANR) , under Grant No. ANR-05-NANO-074  ( HR-RXRS ).
Inundation dynamics are the primary control on greenhouse gas emissions from peatlands. Situated in the central Congo Basin, the Cuvette Centrale is the largest tropical peatland complex. However, our knowledge of the spatial and temporal variations in its water levels is limited. By addressing this gap, we can quantify the relationship between the Cuvette Centrale's water levels and greenhouse gas emissions, and further provide a baseline from which deviations caused by climate or land-use change can be observed, and their impacts understood. We present here a novel approach that combines satellite-derived rainfall, evapotranspiration and L-band Synthetic Aperture Radar (SAR) data to estimate spatial and temporal changes in water level across a sub-region of the Cuvette Centrale. Our key outputs are a map showing the spatial distribution of rainfed and flood-prone locations and a daily, 100 m resolution map of peatland water levels.
They calculated the mean annual variation in the CRB SWS as 81 ± 24 km 3 , which aligns with more recent findings from Kitambo et al. [27] , who estimated monthly SWS dynamics over the CRB at 0. 25°resolution, for the period 1995 to 2015, using a combination of water level heights from virtual altimeter stations and the water extent from the updated GIEMS-2 dataset  [28]. They found strong seasonal and inter-annual variability in the SWS, with the Middle-Congo sub-basin showing the highest variability in the mean amplitude of the SWS volume. Land-surface modelling techniques have also been used to estimate the CRB surface water extent dynamics; for example, Tshimanga and Hughes  [14]  applied the Pitman model, a semi-distributed rainfall-runoff model, across the CRB, with the aim of understanding the processes of runoff generation.
The studies highlighted so far have improved our understanding of the surface water extent and storage dynamics of the central Congo Basin, but are limited in their usefulness for assessing surface-water dynamics for the non-flood-prone regions of the Cuvette Centrale peatland complex. Altimeters primarily measure water levels over permanent water bodies, including lakes and rivers, and interpolation of these data is, therefore, best suited for flood-prone regions, and would not be representative of ombrotrophic (rainfed) peatland regions. There remains a gap in our knowledge relating to inundation dynamics across the ombrotrophic peatland regions  [32, 33]. Alternatively, direct in situ water table data may be used, and some are available from water loggers installed in the Cuvette Centrale region  [32, 33] ; however, they are limited in their spatial and temporal coverage.
While previous studies have succeeded at mapping surface water extent and it has been shown that L-band SAR data can be used to map water levels in densely forested regions  [15] , we lack high spatiotemporal resolution maps of water level variation that can capture inundation dynamics over rainfed regions of the Cuvette Centrale. To address this, we used the high temporal (daily) pattern of net water input (rainfall minus evapotranspiration, not accounting for run-off or groundwater inputs) together with the less frequent but higher spatial resolution (100 m) L-band ALOS-2 PALSAR-2 ScanSAR data, which, at approximately 23. 5 cm wavelength, can effectively see through the canopy to the peat surface, to interpolate the SAR imagery to daily resolution. We then used in situ water table depth measurements, recorded during expeditions along multiple transects in the RoC and DRC  [29, 33] , to develop a transfer equation between the SAR backscatter and water level, enabling us to estimate the daily evolution of water level across a sub-region of the Cuvette Centrale.
How does the inundation of tropical peatlands in the Cuvette Centrale vary spatially and temporally? 2. How does water level correlate with net water input across the peatlands, and can this be used to distinguish between areas of the Cuvette Centrale that are largely rainfed and where flood or additional groundwater dynamics play a significant role? 3. What are the differences in peatland hydrological inputs to the east and west of the Congo River? The use of L-band SAR data to map surface-water dynamics on a high resolution, e. g. , a 1-hectare grid, rather than for delineating the extent of flooding, as has been carried out in previous studies, enables more precise mapping of water levels across the peatlands. This is required for bottom-up estimation of GHG emissions from the peatlands.
SAR Data The ALOS-2 satellite mission was launched in 2014 by the Japan Aerospace Exploration Agency (JAXA), with the Phased Array L-band SAR 2 (PALSAR-2) instrument on board. One of its main objectives was to monitor environmental changes globally, including the inundation dynamics of wetland regions  [42]. We used 17 dual-polarisation PALSAR-2 scenes obtained through the JAXA G-Portal interface (https://gportal. jaxa. jp/gpr/?lang=en, last accessed: 13 April 2023) in CEOS format at the level 1. 5 geo-referenced stage of processing. These data include a basic radiometric correction but not speckle reduction, terrain correction, or geo-referencing. The polarisations they include are the Horizontally-transmitted, Horizontally-received (HH) and Horizontally-transmitted, Vertically-received (HV) amplitude and intensity bands.
Yuan et al. [43]  used the ALOS PALSAR HH polarisation together with altimetry data to map water level changes over Ĩle Mbamou in the Congo Basin, due to its higher sensitivity to water level  [40]. The HV polarisation is more sensitive to vegetation characteristics, and the ratio of the two (HH:HV) has previously been used to improve identification between flooded and non-flooded pixels when using Sentinel-1 SAR data at C-band resolution  [44]. All images obtained were taken in the descending orbit direction. The time series of 17 collocated SAR images we use includes a set of 15 subsequent scenes from 2019 to 2020, each separated by 42 days in most cases and 28 days in two cases, and a second set of two subsequent scenes from 2021. There is a gap of ∼11 months between the end date of the first set and the start date of the second.
Meteorological Data Rainfall is highly variable across the Cuvette Centrale both spatially and temporally, and our study requires sufficiently high-resolution data to assess how rainfall variations relate to temporal changes in backscatter in the PALSAR-2 imagery. We use the daily Climate Hazards Center Infrared Precipitation with Stations version 2 (CHIRPS-2) rainfall dataset  [45] , available at 0. 05 • resolution (5. 55 km resolution at the equator). An intercomparison study of remotely sensed rainfall products showed it to be the highest resolution product available that was effective at reproducing the spatial variability of Central African rainfall and performed well on the inter-annual scale  [46]. Additionally, Santos et al. [47]  highlighted the benefits of using CHIRPS-2 rainfall data within the Soil and Water Assessment Tool (SWAT) model over the Congo Basin, in comparison to other satellite-derived rainfall data.
Land Cover and Terrain Data We used an approximately 50 m resolution land cover map of the Cuvette Centrale region, developed by Crezee et al. [29] , to mask our data for permanent water bodies and non-peatland land class areas. Crezee et al. [29]  included ALOS PALSAR yearly mosaic HH polarisation data in their land cover map model but found that it did not distinguish between peat swamp vegetation classes. We, therefore, use ALOS-2 PALSAR-2 HH data to explore the differences in derived water levels between the two dominant peatland vegetation classes identified by Dargie et al. [11]  and Crezee et al. [29] : palm and hardwood swamp. In our discussion of the results, we refer to terrain data, including the MERIT Hydro Digital Elevation Model (DEM) and Height Above Nearest Drainage basin (HAND) datasets  [51]  at 90 m resolution, retrieved from Google Earth Engine; the slope of the terrain, which we derived from the MERIT DEM using GDAL tools and the Relative Topographic Position Index (RTPI) which we calculated from the DEM using the Whitebox tools Python package.
Calculation of Daily Net Water Input We re-gridded the 0. 05°CHIRPS to the same resolution as the AET data (0. 01°) using a bilinear interpolation. This was carried out only for the purpose of the data grids being co-located, rather than to enhance the resolution of the rainfall data. To approximate the daily AET, we applied the temporal pattern of the daily PET data to the dekadal (10-day) AET data. This involved summing the daily PET over the same dekadal periods as the AET data and calculating daily weightings, corresponding with the distribution of the daily PET over this period. We then applied these daily weightings to the dekadal AET to estimate daily AET. This method effectively applied a pixel-by-pixel multiplying factor to the daily PET to calculate the daily AET from the pattern of the PET.
Determining Which Areas Are Rainfed We calculated pixel-wise temporal correlations between the change in backscatter between pairs of subsequent images in the PALSAR-2 HH time series stack (δHH) and the time series of the corresponding accumulated net water input between each pair of subsequent images. We used the resulting pixel-wise correlation statistics, including the correlation, slope, p-value and standard error, to determine the extent to which each pixel can be classed as rainfed, receiving its water inputs from direct rainfall. We also calculated the number of days of net water accumulation that results in the highest correlation between these two datasets, allowing us to map more clearly the extent to which different areas are directly rainfed, receive additional surface or sub-surface hydrological input or are flood-prone.
Use of Elevation Data in the Discussion of the Results The relative topographic position index (RTPI) is a measure of the difference in elevation between a pixel and the mean elevation of its surrounding pixels within a defined filter box extent  [54]. We derived the RTPI from the 90 m MERIT DEM using the Whitebox Tools Python package RTPI function, with a 56 by 56 (approximately 5 × 5 km) filter kernel.