H.O. recognises support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) within the CRC 1585 MultiTrans. Furthermore, H.O. and T.S. gratefully acknowledge DFG funding from project PolarBatt (OB 425/13-1) and support from the DFG IRTG 2818 OPTEXC. Computations were carried out on the HPC systems festus (DFG grant number 523317330) of the University of Bayreuth, Fritz (NHR@FAU grant number b243cb, DFG grant number 440719683) of the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), and Raven at the Max Planck Computing and Data Facility.
", "type": { "id": "notes", "title": { "de": "Anmerkungen", "en": "Notes" } } } ], "creators": [ { "affiliations": [ { "name": "University of bayreuth" } ], "person_or_org": { "family_name": "Singh", "given_name": "Tavinder", "identifiers": [ { "identifier": "0000-0002-0184-004X", "scheme": "orcid" } ], "name": "Singh, Tavinder", "type": "personal" }, "role": { "id": "researcher", "title": { "de": "WissenschaftlerIn", "en": "Researcher" } } }, { "affiliations": [ { "id": "03k9qs827", "identifiers": [ { "identifier": "03k9qs827", "scheme": "ror" }, { "identifier": "grid.418028.7", "scheme": "grid" }, { "identifier": "0000 0001 0565 1775", "scheme": "isni" } ], "name": "Fritz Haber Institute of the Max Planck Society" } ], "person_or_org": { "family_name": "Chan", "given_name": "Yu-Te", "identifiers": [ { "identifier": "0000-0002-6452-4116", "scheme": "orcid" } ], "name": "Chan, Yu-Te", "type": "personal" }, "role": { "id": "researcher", "title": { "de": "WissenschaftlerIn", "en": "Researcher" } } }, { "affiliations": [ { "id": "028wq3277", "identifiers": [ { "identifier": "028wq3277", "scheme": "ror" }, { "identifier": "grid.462034.7", "scheme": "grid" }, { "identifier": "0000 0001 2368 8723", "scheme": "isni" } ], "name": "Institut Charles Gerhardt Montpellier" } ], "person_or_org": { "family_name": "Athar", "given_name": "Shoeb", "identifiers": [ { "identifier": "0009-0001-2374-3769", "scheme": "orcid" } ], "name": "Athar, Shoeb", "type": "personal" }, "role": { "id": "projectmember", "title": { "de": "Projektmitglied", "en": "Project member" } } }, { "affiliations": [ { "id": "03k9qs827", "identifiers": [ { "identifier": "03k9qs827", "scheme": "ror" }, { "identifier": "grid.418028.7", "scheme": "grid" }, { "identifier": "0000 0001 0565 1775", "scheme": "isni" } ], "name": "Fritz Haber Institute of the Max Planck Society" } ], "person_or_org": { "family_name": "Scheurer", "given_name": "Christoph", "identifiers": [ { "identifier": "0000-0002-7227-8672", "scheme": "orcid" } ], "name": "Scheurer, Christoph", "type": "personal" }, "role": { "id": "projectmember", "title": { "de": "Projektmitglied", "en": "Project member" } } }, { "affiliations": [ { "id": "03k9qs827", "identifiers": [ { "identifier": "03k9qs827", "scheme": "ror" }, { "identifier": "grid.418028.7", "scheme": "grid" }, { "identifier": "0000 0001 0565 1775", "scheme": "isni" } ], "name": "Fritz Haber Institute of the Max Planck Society" } ], "person_or_org": { "family_name": "Kick", "given_name": "Matthias", "identifiers": [ { "identifier": "0000-0003-3408-9886", "scheme": "orcid" } ], "name": "Kick, Matthias", "type": "personal" }, "role": { "id": "projectmember", "title": { "de": "Projektmitglied", "en": "Project member" } } }, { "affiliations": [ { "name": "Chair for Theoretical Physics VII and Bavarian Center for Battery Technologies, University of Bayreuth, Universit\u00e4tsstr. 30, 95447 Germany" } ], "person_or_org": { "family_name": "Oberhofer", "given_name": "Harald", "identifiers": [ { "identifier": "0000-0002-5791-6736", "scheme": "orcid" } ], "name": "Oberhofer, Harald", "type": "personal" }, "role": { "id": "projectleader", "title": { "de": "ProjektleiterIn", "en": "Project leader" } } } ], "description": "This dataset contains structures, distance descriptors and relative energies of two-polaron configurations in two different defected supercells of Li4Ti5O12. Additionally, it contains jupyter notebooks with XGBoost models used to predict the energies of polarons in each supercell from the given descriptors.
\nThe main directory contains the following two subdirectories:
\nlto_cell_A: This directory contains two subdirectories, opt_pol_structures and Notebook_ML_Model.\nopt_pol_structures: It contains directories for the DFT+U optimized geometry files, including occupation matrices for different polaronic configurations for LTO_Cell_A, used for training and testing the machine learning model.Notebook_ML_Model: It contains the following files: \nall_calculated_3.dat: This file contains the DFT calculated relative energies and features corresponding to different polaronic configurations for LTO_Cell_A.polaron_data: This file contains features for all possible 780 polaronic configurations for the LTO simulation cell A.XGB_reg.ipynb: This Jupyter notebook is used for training and evaluating the XGBoost regression model. It includes data preprocessing, model training, and evaluation steps, as well as visualizations of the results.lto_model.json: This file contains the saved XGBoost model in JSON format.training.dat and test.dat: These files contain the training and testing datasets used for the model. lto_cell_B: This directory also contains two subdirectories, opt_pol_structures and Notebook_ML_Model. \nopt_pol_structures: Similar to the one in lto_cell_A, it contains directories for the DFT+U optimized geometry files for LTO_Cell_B.Notebook_ML_Model: It contains the following files: \nall_calculated.dat: This file contains the DFT+U calculated relative energies and features corresponding to different 196 polaronic configurations for LTO_Cell_B used for training and testing purposes.all_feature.dat: This file contains features for all possible 74880 polaronic configurations for the LTO simulation cell B.XGB_reg.ipynb: This Jupyter notebook is used for training and evaluating the XGBoost regression model for LTO_Cell_B. It includes data preprocessing, model training, and evaluation steps, as well as visualizations of the results.lto_model.json: This file contains the saved XGBoost model in JSON format for LTO_Cell_B.training.dat and test.dat: These files contain the training and testing datasets used for the model in LTO_Cell_B.lto_cell_C: This directory also contains two subdirectories, opt_pol_structures and Notebook_ML_Model.\nopt_pol_structures: Similar to the one in lto_cell_A, it contains directories for the DFT+U optimized geometry files for LTO_Cell_C.Notebook_ML_Model: It contains the following files: \nall_calculated.dat: This file contains the DFT+U calculated relative energies and features corresponding to different 188 polaronic configurations for LTO_Cell_C used for training and testing purposes. XGB_reg.ipynb: This Jupyter notebook is used for training and evaluating the XGBoost regression model for LTO_Cell_C. It includes data preprocessing, model training, and evaluation steps, as well as visualizations of the results.training.dat and test.dat: These files contain the training and testing datasets used for the model in LTO_Cell_C. https://www.baybatt.uni-bayreuth.de/de/index.html
\r\n\r\nThe Bavarian Center for Battery Technology (BayBatt) bundles battery-specific expertise in physics and chemistry, material science and engineering, as well as information technology and economics as a supra-regional competence center at the Bayreuth location.
\r\n\r\nIts central task is the interdisciplinary research and development of battery storage systems at the interfaces of materials science, electrochemistry, engineering, information technology and economics, as well as university teaching in the field of battery storage systems.
\r\n\r\nThe research focuses on secure high-performance materials, interfacial phenomena and transport processes, smart battery, and networked storage and sustainability encompasses all scales of battery research from atomistic structure, particles, cell components and cell, battery module and battery system to the higher-level energy system.
\r\n\r\nWith this approach, battery systems are researched and developed as an interacting and intelligent system from modules, cells and battery management with the associated mechanical, thermal and electrical components to the functional and active materials of an electrochemical cell.
\r\n\r\nApplications of the developments are in the field of electromobility, power tools as well as in the stationary sector. BayBatt aims to build bridges between basic electrochemical, materials science and engineering research and the industrial use of the developed processes and models.
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