dataesr/scientific-paragraphs-categorization huggingface dataset:
Models implementation in practice In terms of physics-based models, the manufacturing process of LIBs has been mainly considered using CGMD and DEM simulation techniques. Later steps include electrolyte infiltration, which requires fluid dynamics simulations and electrochemical simulations that FEM can implement. Table  3  presents some examples of software that can be used for these purposes. LAMMPS is an open-source classical MD code developed by researchers from the US Department of Energy in collaboration with other private centers and universities. The code, written in C + + , is also parallelized with MPI, allowing for very efficient and fast simulations. LAMMPS's objective materials are solid-state systems such as metals or semiconductors and soft matter systems like biomolecules and polymers, as long as the modeled interactions are short-ranged and particle densities are moderately bounded.
LAMMPS has available the so-called ML potentials, which replace the physics equations fit a set of experimental or calculated data with generic equations that describe the geometry of a neighborhood of atoms and are refined with an increasingly large dataset. [77] IGGGHTS is another open-source software developed by CFDEM® project based on LAMMPS, but with a special focus on DEM for industrial applications. For this purpose, it expands on the latter basic capabilities to include the possibility of importing complex geometries, moving meshes, improvements on the code dealing with granular interactions, description of heat conduction, and the possibility of coupling with CFD simulations, among others. [78] alabos is a C + + library for CFD simulations based on the LBM. It can be accessed through Python or Java interfaces, facilitating its use and easily setting up simulations without the need for a deep understanding of the LBM algorithms.
Additionally, it works on various platforms due to its general lack of dependencies. Palabos takes advantage of the highly parallelizable nature of LBM to allow for the use of MPI libraries that work efficiently, leading to a significant improvement in calculation times. [79] OMSOL Multiphysics and Abaqus allow running FEM and DEM simulations for mechanical, electrical, and electrochemical applications. Simcenter STAR-CCM + is a multiphysics CFD software that focuses on optimization. Simcenter Battery Design Studio allows the simulation of LIB cell performance with a wide array of battery components and materials. Due to the improved interconnectivity of devices, the overall increase in computational power and a higher degree of data availability enable applications of data-driven methods.
These methods are capable of extracting (or mining for) desired information from vast amounts of data. Many of these methods, such as ML or AI, are commonly applied in everyday situations (e. g. , self-driving cars and recommendation systems in online shopping). Although these algorithms and methods are highly sophisticated and complex, their application hurdle has been greatly reduced over recent years. This has been achieved by developing various data analytics tools (e. g. , RapidMiner, KNIME, ELKI). The former requirement of being capable of creating your code or algorithms is no longer given using these tools. Many data analytics tools offer built-in functions that only have to be parameterized before analyzing the data. However, programming and scripting languages (e. g. , Python, R, Matlab) are still widespread in the scientific world.
KNIME (Konstanz Information Miner) Java-based free and open-source data analytics and reporting platform Node-based workflow data loading, transformation, and preprocessing, visualization, analytics and modeling, evaluation, and deployment Bockwinkel et al. [86]  R Interpreted programming language Many specialized libraries for statistics, mathematics, science, etc. Can implement code from C & C + + Kornas et al. [87]  Cunha et al. [33]  Kirchhof et al. [88]  Meyer et al. [89]  Others ELKI (Environment for developing KDD-Applications Supported by Index-Structures) Weka (Waikato Environment for Knowledge Analysis) SPSS H2O. ai Kornas, et al. [90]  Matlab Proprietary programming language Allows faster and computationally more efficient matrix operations compared to other programming languages Many specialized libraries for statistics, broad engineering fields, computer vision, ML, etc.
These data sources contain data providing different information, e. g. , energy demand, intermediate product structures, production times, or ambient conditions. A connection to the physical data access point must be established to acquire data from these sources. These physical data access points are hardware through which the data of the data source can be obtained (e. g. , PLC, sensor, measuring device, server). The data can then be acquired through a data acquisition interface. This interface can be communication protocol (e. g. , Profinet, OPC UA, Modbus), analog/digital signals, connection to a file (e. g. , Excel files, word files, image files), or a database (e. g. , SQL, NO-SQL) connection. The acquired data can be of different formats, such as time series, spatial, and discrete data, and can be available either live or as stored historical data.
MediaWiki provides the core document and file storage with extensive interfaces for humans (editors) and machines (APIs). Templates and forms ensure uniformity and efficiency. The extension Semantic MediaWiki provides the Wiki with numerous possibilities for linking and annotating data. In addition, semantic queries can be sent directly to the Wiki or its SPARQL endpoint, and results can be visualized in various ways. The core (meta) data structure is flanked by a time series database specialized in continuous data acquisition. Such data streams can be transmitted from the laboratory using Node-RED, especially from OPC-UA capable devices. Finally, users can access via Jupyter-Lab all data stored in the system, process it, and push the results back. The project is currently under heavy development and is planned to get released within the next month.
The BVCO also interacts directly with the General Process Ontology (GPO),  [194]  which describes terms common across different process engineering domains. The BVCO is being developed under the coordination of Fraunhofer ISC as part of both EU and national research projects. It is hosted in a public repository on GitHub  [195]  and available for use under a creative commons CC-BY-4. 0 license. In terms of synthetic data from models, one approach for generation is the ARTISTIC project online calculator. [196] This is a freely accessible platform with a user-friendly interface that gives access to 3D manufacturing models. The manufacturing steps currently include the slurry phase, drying, and electrode calendering. It is envisioned to expand these to electrolyte infiltration and electrochemical characterization.
The integration of modeling tools with such other technologies is already possible. Nonetheless, it highly depends on the motivation of battery laboratories and companies to launch digital transformation programs with an end-to-end integrated digital platform. Then, we reported a novel classification of LIB manufacturing data types to develop digital twins of prototypinglines. This classification can be seen as a data model or metadata template. It was performed regarding the criticality of developing different process models (based on empirical, continuum, discrete, or ML approaches), the accuracy of the experimental measurements of relevant properties (e. g. , coating thickness), and the possibility of not measuring in real-time. Materials science, process engineering, characterization aspects, computational modeling, and data sciences support such classification results from a collaborative transdisciplinary effort.
It also constitutes a solid background for battery manufacturing data standardization and ontology initiatives such as the ones mentioned in the roadmap of the European Battery 2030 + research program or the ones that could be undertaken in future European projects. Last but not least, it is worth mentioning that several of the data types discussed in our study can also be found in production lines. Therefore, we are confident that this work also provides useful information for the industry. From an industrial perspective, the parameters mentioned above related to the entire manufacturing process are of utmost importance. These parameters are typically measured and controlled to ensure a stable process and, in the end, a safe product for the customers. Enhancing the insights one gets from these parameters through the presented approaches will surely lead to beneficial developments on the production scale, especially for the quality and the reduction of energy consumption throughout the entire process.
772873, "ARTISTIC" project). A. A. F. and F. M. Z. acknowledge the European Union's Horizon 2020 research and innovation program under grant agreement No 957189 (BIG-MAP). A. A. F. acknowledges Institut Universitaire de France for the support. E. A. , M. D. , and A. A. F. acknowledge the ALISTORE European Research Institute for funding support. E. A. , I. B. , F. H. and C. B. gratefully acknowledge financial support from the  European Union 's  Horizon 2020 Research and Innovation Programme  within the project " DEFAC-TO " (grant agreement No  875247 ). The LiPLANET Project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement N. 875479. C. B. acknowledges support by the  Federal Ministry of Education and Research (BMBF)  within the Project " Sim4Pro ", (grant no  03XP0242A ).
Compensating substitutions happen when one mutation is advantageously selected because it restores the loss of fitness induced by a previous deleterious mutation. How frequent such mutations occur in evolution and what is the structural and functional context permitting their emergence remain open questions. We built an atlas of intra-protein compensatory substitutions using a phylogenetic approach and a dataset of 1,630 bacterial protein families for which high-quality sequence alignments and experimentally derived protein structures were available. We identified more than 51,000 positions coevolving by the mean of predicted compensatory mutations. Using the evolutionary and structural properties of the analyzed positions, we demonstrate that compensatory mutations are scarce (typically only a few in the protein history) but widespread (the majority of proteins experienced at least one).
2007 ). The frequency of compensating mutations in proteins, their distribution with regard to structural properties, and their relation to the evolutionary rate of coevolving sites remain to be established  (Starr and Thornton 2016). Here, we aim at generating an atlas of protein coevolution. We gathered a large dataset of sequence alignments of protein families for which at least one protein structure has been experimentally determined. We use a phylogenetic method that exhibits positions undergoing substitutions on the same branches of the phylogeny. (Dutheil et al. 2005; Dutheil and Galtier 2007). We further restrict our analysis to the case of coevolution by compensating mutations, where the first mutation at a given position has a negative fitness effect, for instance, because it results in a less stable protein structure, which is compensated by a second mutation at another position in the protein.
Results We gathered a large data set of protein sequence families from complete genome sequences and for which structural information was available for at least one representative sequence. We initially analyzed gene families from the Archaea, Eukaryotes and Bacteria separately. However, we later restricted our analyses to bacterial families due to the small number of sequences and structures retained in the two other domains. We developed a stringent pipeline controlling for alignment uncertainty and sequence redundancy (see Material and Methods). Our curated data set contains 1,630 protein families, with a number of sequences per family ranging from 100 to 400 (Supplementary    S1 ). In order to assess the compensating nature of substitutions, we considered several biochemical properties.
Following previous studies, we considered the volume, polarity and charge of amino acid residues  (Neher 1994; Pollock et al. 1999; Tufféry and Darlu 2000). The AAIndex database  (Kawashima et al. 2008) , however, contains more than 500 non-independent indices. Using clustering techniques, Saha et al. (Saha et al. 2012 ) have shown that these redundant properties are clustered into eight groups. We included the eight indices corresponding to the centres of these clusters (hereby referred to as "synthetic indices"), in order to provide an objective and comprehensive measure of amino acid biochemical properties. These indices are defined as follow  (Saha et al. 2012 ): electric properties (I1), hydrophobicity (I2), alpha and turn propensities (I3), physicochemical properties (I4), residue propensity (I5), composition (I6), beta propensity (I7), and intrinsic propensities (I8).
Substitution mapping enables the detection of compensating mutations We detail two candidate groups to illustrate the nature of the positions predicted as coevolving in our data set. The first group involves a pair of distant sites at positions 146 and 237 in the sequence alignment of protein family HOG000218359. The three-dimensional structure used as a representative is the Menaquinone biosynthesis MenD protein of Bacillus subtilis (PDB ID: 2X7J). The protein is a tetramer  (Dawson et al. 2010) , and the A chain was used as a reference. The coevolving positions correspond to position Glu-109 and Arg-174 in the B. subtilis sequence (Figure  1A ). The positions are significantly coevolving for charge compensation (P-value = 4. 940333e-06, significant after correction for multiple testing with a false discovery rate (FDR) of 1%).
D Material and Methods): the compensation is perfect (C = 1) for the first branch and almost perfect for the second branch (C > 0. 9). The second example group involves three positions from the HOG000227724 family, whose representative structure is the A-chain of the dTDP-6-deoxy-D-xylo-4-hexulose 3,5 epimerase (RmlC) of Salmonella typhimurium (PDB ID: 1DZR, Figure  2A )  (Giraud et al. 2000). The coevolving positions include the two close residues Glu-16 and Phe-20, as well as Asn-150. The three residues are located at the surface of the protein but are not in contact. They were detected as coevolving for volume compensation (P-value = 0. 0007818069), with several branches showing substitutions leading to a change in residue volume (Figure  2B ). At least six branches show a significant signal of compensation (Figure  2C-D ).
There is comparatively less coevolution within secondary structure motifs We used mixed generalized linear models to assess the impact of structural properties on the propensity of sites to coevolve. Each biochemical property was analyzed separately, and each site was considered an independent data point. The protein family was treated as a random effect, allowing sites within the same protein to share the same error distribution, while different protein families may have distinct ones. Whether each site was found to be coevolving was used as a binary response variable. Structural properties of each site were recorded, including its relative solvent accessibility (RSA), its secondary structure motif (one of α-helix, 3-10 helix, π-helix, strand, β-bridge, turn, bend, intrinsically disordered or unknown), and the evolutionary rate of the site.
Therefore, we conclude that coevolving residues are more likely to be in contact in the three-dimensional structure. This effect is not due to their slow evolutionary rate and low solvent exposure. Using a similar approach, we further investigated whether coevolving residues in a given secondary structure motif were located in the same element. We generated two sub-datasets, only containing sites in helices or strands, respectively. We then computed the connectivity graphs in each case and considered two residues in contact if they were in the same helix, strand, or sheet. By sampling among helix or strand sites within each protein family, we computed the expected distribution of these statistics. We also compared the results with randomization where sites were constrained to have similar evolutionary rate or RSA.
Sequence retrieval Protein sequences were retrieved from the HOGENOM database (release 06)  (Penel et al. 2009) , which contains families of homologous protein-coding genes from completely sequenced genomes of Bacteria, Archaea and Eukaryotes. We sampled the sequences in all three domains of life and found that only Bacteria had enough sequences to provide sufficient signal for coevolution detection. Thus, we targeted members of the Bacteria domain for this study. Using the 'query_win' retrieval program  (Gouy and Delmotte 2008) , 2,047 families were selected for which at least one bacterial sequence had at least one experimentally solved structure available. All the Archaea and Eukaryotic sequences were removed from the selected families. For each family, species having more than one sequence were discarded to avoid the comparison of paralogous sequences.
To ensure the comparison of families with similar statistical power, we discarded families with less than 100 sequences. We developed a pipeline to process each protein family, with the goal to minimize the amount of missing data and maximize the alignment reliability: 1. Approximate alignment and phylogeny: a fast multiple sequence alignment for each family was performed using Clustal Omega  (Sievers et al. 2011). For each alignment, a first phylogenetic tree was built with FastTree  (Price et al. 2010). 2. Minimization of missing data: the phylogenetic tree obtained at step 1 was used to remove sequences from the alignment in order to maximize the number of sites with sufficient coverage using the 'bppAlnOptim' program  (Dutheil and Figuet 2015). Unresolved characters were considered together with gaps in coverage calculations, and only sites without gaps were kept.
3. High-quality alignment: filtered families were realigned using both Muscle  (Edgar 2004 ) and Clustal Omega  (Sievers et al. 2011). We built a consensus alignment by computing sum-of-pairs scores (SPS) for each alignment site and masking sites with an SPS lower than 80%. These highquality consensus alignments were used in the following analyses. 4. Phylogenetic sampling: we performed a two-step phylogenetic sampling, first to remove highly similar sequences, which do not carry a biological signal, and second to limit the maximum number of sequences in each family in order to reduce computational time. The 'bppPhySamp" program from the Bio++ program suite  (Dutheil and Boussau 2008)  was used to ensure that sequences in each alignment were at least 1% different from the other sequences and that there is a maximum of 400 sequences per alignment.
Families with less than 100 sequences after sampling were further discarded, leaving a total of 1,684 families. 5. Accurate phylogeny reconstruction: the resulting masked alignments were used to build a maximum likelihood phylogenetic tree, using the PhyML program  (Guindon and Gascuel 2003)   (Guindon et al. 2010) , using the LG protein substitution model  (Le and Gascuel 2008)  and a gamma distribution with four rate classes  (Yang 1994). The best tree from Nearest Neighbour Interchange and Subtree Pruning Regrafting algorithms  (Guindon et al. 2010 ) was kept. The inferred phylogenies were used for the identification of coevolving sites.
Identification of coevolving sites Coevolving positions within each protein family was predicted using the CoMap package v1. 5. 2  (Dutheil and Galtier 2007). A false discovery rate (FDR) utilizing the built-in algorithm was then used to correct for multiple testing. In this study, 10,000 simulations per tested group were performed, and the groups with FDR < 1% were considered as coevolving. Compensation was used as a measure of coevolution which assesses the compensatory nature of cosubstitutions based on a given physicochemical property. The compensation indexes used as weights were retrieved from the AAindex database  (Kawashima et al. 2008). Three commonly studied properties: Volume (as defined by Grantham, AAindex id: GRAR740103); Polarity (as defined by Grantham, AAindex id: GRAR740102); Charge (as defined by Klein, AAindex id: KLEP840101) were tested.
In addition, we also tested eight nonoverlapping properties (indexes) as described by  Saha et al. (Saha et al. 2012). Saha et al. have categorized all published 544 amino acid indexes of the AAindex database into eight clusters, and these eights categories were used in this study. Corresponding categories are Electric property; Hydrophobicity; Alpha and turn propensities; Physicochemical properties; Residue propensity; Composition; Beta propensity and Intrinsic propensities. Because the charge property has three discrete states (positively charged, negatively charged and neutral), we further assess the impact of discrete vs continuous properties on the detection of coevolution by discretizing the volume and polarity properties. Residues G (volume  (Grantham 1974) : 3), A (31), S (32), and P (32.
The diameter of each phylogenetic tree (maximum distance between any two leaves) as well as the median of sequence lengths for each family were computed using the 'ape'  (Paradis et al. 2004 ) and 'seqinr'  (Charif et al. 2005)  packages for R, and recorded. The relationship between the number of detected sites and the alignment length was assessed using a broken regression model, as implemented in the 'lm. br' package for R  (Adams 2017).
Evolutionary rates We measured the rate of molecular evolution according to a given biochemical property using the norm of the weighted substitution vector for each site  (Dutheil and Galtier 2007). For a given site in the alignment, the number of substitutions that occurred on each branch of the phylogenetic tree was inferred by probabilistic substitution mapping. Each substitution type from an amino acid A into an amino acid B was then weighted by the absolute difference in the amino acid property for A and B. into positions in the PDB structure. All scripts used for analyses were written in Python using the Biopython modules  (Hamelryck and Manderick 2003). The reference PDB structures were then subsequently used to extract structural properties: secondary structure and relative solvent accessibility were obtained using the DSSP program  (Kabsch and Sander 1983) , run via the Biopython DSSP module.
The distance of each residue to the solvent-accessible surface was computed via the MSMS program  (Sanner et al. 1996) , accessed via the 'Biopython. ResidueDepth' module. Both the residue depth and the Cɑ depths were computed. We further computed the number of residues in contact with each residue using Cɑ distance thresholds of 5, 8 and 10 Å. Because several of these properties may be intrinsically correlated, we examined their relationships using principal component analysis and non-parametric pairwise correlation (Spearman rank correlation). We found that the structural variables were highly correlated (Supplementary Figure  S3 ): residue depths and numbers of contacts were highly positively correlated while highly negatively correlated with RSA. In summary, the more exposed a residue is, the closer it is to the surface of the protein and the less contacts it has with other residues.