dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, a key distinction between sampling the nuclear coordinates via quantum distributions and classical dynamics is the amount of energy deposited in each degree of freedom. In the quantum distribution within harmonic approximation at 0 K, this energy is the harmonic zero-point energy. For a normal mode vibrating at 1000 cm -1 , it amounts to 0. 12 eV. On the other hand, in classical dynamics of a canonical system equilibrated at temperature T, the equipartition principle ensures that each degree must have k B T, which corresponds to 0. 04 eV at 500 K (as employed in the MD17 dataset). Thus, nuclear geometries sampled from a quantum distribution have a much broader distribution than when sampled from classical dynamics. A similar tendency is observed when comparing the data distribution in the atomic forces space.
In this case, the histograms in Fig. 7  show that the norm (Frobenius) of the atomic forces matrices has a broad Gaussian shape centered on 298 kcal mol -1 Å -1 in the WS22 dataset, while in MD17, the data is narrowly distributed around 178 kcal mol -1 Å -1. As for the standard deviation, we found the values of 29 kcal mol -1 Å -1 for MD17 and 57 kcal mol -1 Å -1 for the WS22 dataset. It is worth stressing that, for the toluene dataset, we have considered only one equilibrium geometry (S 0 minimum) as a reference to apply the Wigner sampling. Hence, the coverage of chemical properties in the other nine datasets composing the WS22 database should be even larger since our sampling strategy is applied to different conformational spaces. Finally, the composed sampling strategy used to build the WS22 database gives rise to significant differences in configurational space covering and quantum properties distribution not observed in similar databases such as MD17.
These differences lead to the natural question of how much they may affect the learning performance of typical MLP models. While out of the scope of this paper, one of us has conducted an independent ML study to answer this question. It was found that the WS22 dataset uncovered additional requirements for training and Fig. 5  Distribution of geometries deformation with respect to the minimum energy structure as given by the root-mean-squared deviation (RMSD). The Kabsch algorithm was applied to each pair of geometries for the optimum alignment before calculating the RMSD. testing MLPs  56. One of such requirements is that using independently constructed datasets is paramount for fair testing ML potentials, while currently, many tests are limited to only using the MD17 dataset.
acknowledgements MPJ and MPJ are financially supported by the  European Union 's  Horizon 2020 research and innovation program  under  ERC advanced grant  (grant agreement No  832237 , SubNano). They also acknowledge the  Centre de Calcul Intensif d' Aix-Marseille. POD acknowledges funding by the  National Natural Science Foundation of China  ( No 22003051 ) and the  Fundamental Research Funds for the Central Universities  ( No 20720210092 ).
code availability All the data annotations with the chemical properties were obtained from density functional theory calculations performed with the Gaussian 09 program  43. The set of molecular geometries used as input for these calculations was generated using the Newton-X CS (version 2. 2-B08) package  53  for Wigner sampling together with a Python code for geometry interpolations [https://github. com/virtualzx-nad/geodesic-interpolate] as described in the Methods section. Both programs are open access. A custom Python script was written to extract the relevant information from the Gaussian 09 output files, and it is publicly available in the GitHub repository https://github. com/maxjr82/QCDP. Finally, the Python script to perform the dimension reduction of the molecular geometries with the PCA method is available to download from https://github.
Therefore, this signature of changes in surface conditions on subsurface temperatures can be estimated by subtracting the quasi-equilibrium profile from the measured log, obtaining an anomaly profile. Ground heat flux histories retrieved in this analysis are based on inverting the anomaly profile of each individual subsurface temperature profile. We invert subsurface temperature profiles from the Xibalbá dataset  (Cuesta-Valero et al. , 2021c, a)  to estimate the global long-term ground heat flux history. The Xibalbá dataset consists of 1079 subsurface temperature profiles measured around the world, with a larger number of profiles in the middle latitudes of the Northern Hemisphere. The Xibalbá profiles have been screened by eye to remove logs including non-climatic signals due to processes such as water advection  (Cuesta-Valero et al.
Daily average enthalpy and liquid water content profiles are calculated to a depth of 550 m with a spatial resolution of 1 • (per grid cell) for the Arctic permafrost region. The surface temperature offset caused by snow insulation is represented by a dedicated snow scheme accounting for regional snow characteristics  (Sturm et al. , 2010). Ground stratigraphies which determine both the thermal properties of the ground and the amount and location of ground ice are derived based on soil stratigraphy parameterizations developed for the SURFEX land surface model  (Masson et al. , 2013). Required input data are extracted from multiple global datasets, such as the percentage of sand and clay in the soil  (Masson et al. , 2003; Faroux et al. , 2013) , the soil organic carbon content  (Hugelius et al.
, 2014) , driven by the same ESMs. In total, the ensemble contains 16 global lake simulations, providing lake temperature Table  1. Overview of global lake models used. Detailed descriptions of the models and modelling setup can be found in  Golub et al. (2022). profiles (Table  1 ) for the period 1900 to 2021 on a 0. 5 • by 0. 5 • grid. These simulations are combined with global gridded lake depths from the Global Lake Database v. 3 (GLDB;  Choulga et al. , 2019)  rasterized global lake and reservoir area from HydroLAKES and GRanD databases  (Messager et al. , 2016; Lehner et al. , 2011) , as described in  Vanderkelen et al. (2020). Differently from the cylindrical-lake assumption of  Vanderkelen et al. (2020) , in which the grid cell lake volume is calculated by multiplying lake area and depth, we determine lake and reservoir volumes by estimating lake morphometry with the volume development parameter (V d ), which is a well-established geometric approach  (Håkanson, 1977; Johansson et al.
, 2007). The V d parameter represents the extent to which the lake volume deviates from the volume of a cylinder, thereby indicating whether the lake morphometry is concave or convex. We employ a global constant V d value of 1. 19, which is the median V d derived from the 1 427 688 lake polygons in the GLOBathy dataset  (Khazaei et al. , 2022)  using their mean and maximum depths V d = 3 • d mean d max. Per grid cell, the lake volume is calculated as a "reversed wedding cake" by multiplying the thickness of every discrete lake layer of the lake model with the average area at the layer depth A (z), calculated following  Johansson et al. (2007)  as follows: A (z) = A max (1 -d ref ) 1 + d rel sin d rel f V d , (2) and f (V d ) = 1. 7V -1 d + 2. 5 -2. 4V d + 0. 23V 3 d , (3) with A max (m 2 ) being the surface lake area calculated based on the gridded HydroLAKES distribution; d rel (m) being the relative lake layer depth d rel = z z max , where z max (m) is given by the GLDB lake depth; and finally, V d (-) being the volume development parameter, taken as constant at 1.
Results Estimates of ground heat flux by the new bootstrapping technique described above present smaller values and a narrower uncertainty range than the results from previous estimates using the Xibalbá dataset  (von Schuckmann et al. , 2020; Cuesta-Valero et al. , 2021c). Previous analyses presented a global heat flux of 97 ± 6 mW m -2 for 1960-2018 in comparison with 84. 8 ± 0. 8 mW m -2 for the period 1960-2020 in this study (Fig. 1a ). Both heat flux estimates consider the same subsurface temperature profiles and the same singular value decomposition algorithm to produce inversions of individual logs. 2020 ) is based on deriving two extremal inversions from each individual profile and then considering the global uncertainty as the average of these extremal inversions. The bootstrap approach, nevertheless, derives a set of 1000 different global averages from individual profiles considering a different quasi-equilibrium profile and a different thermal diffusivity each time, retrieving the 2.
5th, 50th, and 97. 5th percentiles of these global averages. The new bootstrap approach, therefore, should be considered to be a better method for aggregating inversions from different temperature profiles. A more detailed comparison between these two aggregation techniques can be consulted in  Cuesta-Valero et al. (2022). The ground heat fluxes from the bootstrap inversion technique are also higher than those from  Beltrami et al. (2002) , which presented 39. 1 mW m -2 for 1950-2000. These large differences between our results and those from  Beltrami et al. (2002)  arise from the use of different inversion methods and from the higher number of more recent subsurface temperature profiles in the Xibalbá dataset than in  Beltrami et al. (2002) , thus including the recent warming of the continental subsurface.
Conclusions and future steps Continental heat storage has been estimated here by considering, for the first time, inland water bodies and permafrost thawing in addition to the land subsurface. All three components present heat gains during the period 1960-2020, with total heat storage increasing more in the last decades of this period (Table  2 ). Determining the continental heat storage from all land components is important to accurately quantify the Earth heat inventory, which is the critical magnitude informing us about future warming and climate change  (von Schuckmann et al. , 2020) , as well as to provide us with an inhttps://doi. org/10. 5194/esd-14-609-2023 Earth Syst. Dynam. , 14, 609-627, 2023 dicator of the heat-dependent impacts on society and ecosystems (Fig. 5 ). Monitoring the evolution of continental heat storage and its three subsystems is, therefore, important, and periodic updates of this analysis are planned at a frequency of 2-3 years in order to incorporate new data and techniques.
Certain aspects of the analysis presented here should be improved in future iterations. New measurements of subsurface temperature profiles are crucial to providing ground heat flux estimates for the last 2 decades of the period of interest. Values of ground heat flux from 2000 to 2020 in this analysis consist of an extrapolation due to the lack of sufficient profiles measured after the year 2000. Also, new measurements in the Southern Hemisphere and the Middle East are necessary to characterize areas without coverage in the global network of subsurface temperature profiles. Ideally, an international organization should gather a copy of all available measured subsurface temperature profiles to ensure the maintenance and accessibility of these valuable records in future decades. Such a safe copy of all logs should lead to lessfragmented datasets, harmonizing the archiving practices and metadata requirements for all records, in contrast to current practices in which individual researchers are responsible for measuring, curating, storing, and distributing the data.
, 2014; Harrison et al. , 2015; Eyring et al. , 2019). However, the potential of continental heat storage as a reference may be hampered by the use of models to determine the evolution of heat storage in permafrost soils and inland water bodies, as observations are preferred for evaluating climate simulations. There are several steps that can be implemented for improving future estimates of ground heat storage. Expanding the number of estimates of ground heat flux has a high priority, as the ground heat storage is the largest term of the continental heat storage. New measurements of subsurface temperature profiles in areas not well represented in the current global database, regions such as northern and central Africa, South America, the Middle East, and southeastern Asia, are important for improving the spatial coverage of the current subsurface temperature dataset.
Furthermore, strengthening the global network of subsurface profiles by repeating measurements at previously measured sites would reduce uncertainties for the warming of the continental subsurface in the last decades. Flux estimates from other datasets can be also considered, such as from FluxNet towers and from satellite remote sensing data. Indeed, there is an increasing population of satellites providing information about land surface conditions and changes in land cover, and several methods are also being developed to obtain accurate estimates of climate variables from satellite remote sensing observations in combination with land observational networks and numerical models  (Balsamo et al. , 2018). Expanding the permafrost areas considered here, particularly the inclusion of the Tibetan Plateau, would be a priority in the next iterations of this analysis.
Further sources of information to retrieve estimates of permafrost heat storage should also be considered in order to increase the confidence of the results obtained here. Ideally, monitoring of liquid water content in permafrost soils along with borehole temperature measurements would form a complementary dataset besides modelling for estimating permafrost heat storage. However, the current observational networks in the Arctic and on the Tibetan Plateau are not equipped to perform such measurements, and their spatial coverage should be expanded to include currently unmeasured zones in the Canadian Arctic and Eurasia  (Biskaborn et al. , 2015). Monitoring sites tend to be near inhabited locations, existing infrastructure, and resource development sites, leaving large areas without coverage  (Smith et al.
, 2021). Furthermore, these land surface model components do not represent excess ice in the ground or ground subsidence, which bias the represented permafrost thawing  (Lee et al. , 2014; Rodenhizer et al. , 2020). Some ongoing efforts are trying to mitigate this lack of information (e. g. O'Neill et al. , 2019; Smith et al. , 2022a) , which could be incorporated into a future version of this analysis. Replacing ERA-Interim forcing with ERA5 data and forcing for the last decades of the 20th century and the first decades of the 21st century should also be considered. The inland waters heat storage estimates could be refined using spatially varying morphometry characteristics to determine lake volumes per grid cell. The availability of new datasets like GLOBathy  (Khazaei et al. , 2022)  and new insights from the upcoming Surface Water and Ocean Topography (SWOT) mission would allow such an approach.
Nevertheless, obtaining such an estimate is challenging due to the lack of appropriate observational datasets. Given the amount of time and the resources required to set up observing systems or to expand existing ones, together with the challenges associated with long-term maintenance and data continuity, an intermediate solution based on including more remote sensing and reanalysis datasets in the analysis should be explored in the near term. Relevant variables derived from satellite observations or assimilated in reanalysis products, such as land surface temperature, leaf area index, or snow cover, could drive the modelling frameworks used here, increasing the role of observations in estimates of continental heat storage. Furthermore, the near-global coverage of remote sensing satellite observations and the homogeneity of reanalysis products, together with the relatively long periods included in the datasets, would allow the derivation of estimates useful for climate studies without waiting for years or decades for observational recording.
FWOTM920 ). The resources and services used in this work were provided by the  VSC (Flemish Supercomputer Center)  and were funded by the  Research Foundation -Flanders (FWO)  and the  Flemish Government. Financial support. This research has been supported by the  Alexander von Humboldt-Stiftung  (grant no. Research Fellowship ), the  Natural Sciences and Engineering Research Council of Canada  (grant no. DG 140576948 ), the  Canada Research Chairs Program  (grant no. CRC 230687 ), the  Canadian Foundation for Innovation and the Digital Research Alliance of Canada  ( Compute Canada, AceNet ), the  Bundesministerium für Bildung und Forschung  (grant no. 01LN1709A ), and the  Fonds Wetenschappelijk Onderzoek  (grant no. FWOTM920 ). The article processing charges for this open-access publication were covered by the  Helmholtz Centre for Environmental Research -UFZ.
F. J. Cuesta-Valero et al. : Continental Heat Storage    Code and data availability. The subsurface temperature profiles from the Xibalbá dataset were used to derive ground heat fluxes and are available in Cuesta-Valero et al. (2021a). All ISIMIP2b global lake simulations used are publicly available through the ISIMIP repository (https://doi. org/10. 48364/ISIMIP. 931371, Marcé et al. , 2022). The HydroLAKES dataset is available at https://doi. org/10. 1038/ncomms13603 (Messager et al. , 2016), the GRanD dataset is available at https://doi. org/10. 1890/100125 (Lehner et al. , 2011), and the GLDB dataset is available at https://doi. org/10. 3402/tellusa. v66. 21295 (Choulga et al. , 2014). The scripts used for the inland-water heat storage calculations are available at https://doi. org/10.
Results and discussion Hit identification. We performed structure-based virtual screening (SBVS) using the crystal structure of a folate-specific transporter ECF-FolT from Lactobacillus delbrueckii (PDB ID: 5JSZ)  29. We chose this hit-identification strategy considering that for this protein a robust, albeit low-throughput activity assay is available  29  and that the availability of crystal and cryo-EM structures might help to rationalize the binding mode of our novel chemical structures. In the SBVS, we used the algorithm DoGSiteScorer (Supplementary Notes 1, Supplementary Figs. 1  and 2 )  30  to select druggable pockets and, at the outset, we deprioritized the substrateand ATP-binding pockets, because we were primarily interested in allosteric inhibitors. Therefore, we initially selected the so-called P2 pocket as the most promising allosteric binding site; it is in a location near the predicted surface of the membrane, where the EcfT component may change the conformation of two so-called coupling helices during transport (Supplementary Figs.
The S-components interact with a shared ECF module. b Chemical structure of compound 1 identified through structure-based virtual screening using the crystal structure of Lactobacillus delbrueckii ECF-FolT2 (PDB ID 5JSZ)  29. the vitamin across the membrane with an IC 50 value of 282 μM (Supplementary Notes 3, Supplementary Fig. 5 ) and we selected this compound for our studies (Fig. 2 ). At an inhibitor concentration of 5 mM, compound 1 was able to completely inhibit the transport of folate by ECF-FolT2 (Fig. 2b ). Next, we set out to validate that the inhibitory activity of 1 is not due to an artifact-like disruption of the lipid bilayer or chelation of Mg 2+ , which is crucial for the hydrolysis of ATP, or direct interference with the hydrolysis of ATP. Thus, we used the ABC transporter OpuA from L.
3a ), all compounds simulated tend to be located in the membrane. However, the removal of the carboxylate group (in relation to compound 1) makes compound 13 too hydrophobic. The replacement of the hydroxyl group in compound 1 (Fig. 3b ) by an NHBoc group (Fig. 3c ) in compound 14 promotes additional hydrophobic interactions of the tert-butyl group with Leu 172. At the same time, the hydrogen bond of the OH group is maintained by the NHBoc group of compound 14. These combined effects possibly explain the higher activity of 14. As Fig. 3  Unbiased coarse-grained (CG) molecular dynamics simulations of inhibitors binding to ECF transporter. a Crystal structure of ECF FolT2 (PDB ID 5JSZ), in complex with EcfA and EcfA' shown in blue and red, EcfT in white, and S-component in gray. Pocket P2, used in the structure-based virtual screening campaign is highlighted in yellow.
Synthesis and characterization of compounds. The compounds have been synthesized as described in the Supplementary Notes 4. All the compounds have been fully characterized by proton, carbon, and HRMS (Supplementary Figs. 6 7 8 9 10 11 12 13 ). Coarse-grained molecular dynamics simulations. All simulations were performed with the program package GROMACS (version 2018. 8)  41  and the Martini 3 Coarse-Grained (CG) force field  35. The CG protein model was generated with Martinize2 42 using the structures of the apo form of the ECF FolT2 (PDB code: 5JSZ)  29  and ECF PanT (PDB ID 6ZG3)  40  as initial references. A customized elastic network was used to keep the overall structure of ECF complexes. CG models of compounds 1, 13, and 14 were obtained according to the parametrization rules of Martini 3, as described elsewhere  34, 35, 43.
INSANE code  44  was used to build the initial simulation boxes of ECF complexes embedded in a bacterial membrane model composed of 1-palmitoyl-2-oleoyl-snglycero-3-phosphoethanolamine (POPE), 1-palmitoyl-2-oleoylsn-glycero-3-phosphoglycerol (POPG) and cardiolipin (CL) in a ratio of 70:25:5. For each system (ECF FolT2 with compounds 1, 13, and 14 and ECF PanT with compound 1), a total of 10 MD simulation replicas were performed, with each production simulation carried out for 30 µs. Therefore, in a total sampling of 300 µs was performed for each system. Analyses of the trajectories were performed with VMD  45  and GROMACs tools (version 2018. 8)  41. Backmapping from CG representative snapshots to the atomistic resolution was performed with the backward code  46. More technical details about the CG models, system setups, simulation settings, analysis, and backmapping are given in Supplementary Notes 5.
Acknowledgements A. K. H. H. and D. J. S. gratefully acknowledge funding from the  Netherlands Organization for Scientific Research (ChemThem  and  VIDI grants ). A. K. H. H. acknowledges funding from the  European Research Council  ( ERC  starting grant  757913 ), the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement no. 713482 , and the  Helmholtz-Association's Initiative and Networking Fund. D. J. S. acknowledges support from the  European Research Council  ( ERC PoC  grant  812867 ). I. S. received a scholarship from the  Indonesia Endowment Fund for Education  ( LPDPLembaga Pengelolaan Dana Pendidikan ,  Departemen Keuangan, Republik Indonesia). S. B. received the  ALERT grant  (no. 665250 ), the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement no.
Code availability All software used for this study are available and the links are provided as follows. For virtual screening, it was used BioSolveIT https://www. biosolveit. de/ and DoGSiteScorer for druggability assessment. For MD simulations and analyses with GROMACS, the code is available at https://www. gromacs. org/. For visualization of the MD trajectories and analysis with VMD, https://www. ks. uiuc. edu/Research/vmd/. Martini CG Force-field parameters, codes, and procedures (e. g. tutorials) are publicly available at http:// cgmartini. nl, https://mad. ibcp. fr/ and https://github. com/Martini-Force-Field-Initiative.