Janosh Riebesell

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Bowen DengUC BerkeleyVerified email at berkeley.edu
Peichen ZhongUC Berkeley, Lawrence Berkeley National LaboratoryVerified email at berkeley.edu
Gerbrand CederProfessor of Materials Science and EngineeringVerified email at berkeley.edu
Kristin Aslaug Persson (nee Einarsdott...Department of Materials Science and Engineering, UC BerkeleyVerified email at berkeley.edu
Christopher J. BartelChemical Engineering and Materials Science, University of MinnesotaVerified email at umn.edu
Anubhav JainLawrence Berkeley National LabVerified email at lbl.gov
Rhys GoodallResearcherVerified email at cantab.ac.uk
Philipp BennerFederal Institute for Materials Research and Testing; Max Planck Institute for Molecular GeneticsVerified email at bam.de
Alpha LeeUniversity of CambridgeVerified email at cam.ac.uk
Alex GanoseImperial College LondonVerified email at imperial.ac.uk
Jimmy-Xuan ShenResearch Scientist Microsoft QuantumVerified email at microsoft.com
Janine GeorgeBundesanstalt für Materialforschung und -prüfungVerified email at bam.de
Gian-Marco RignaneseDirecteur de Recherches F.R.S.-FNRS / Professeur UCLouvainVerified email at uclouvain.be
Matthew L EvansBEWARE Research Fellow, Université catholique de LouvainVerified email at uclouvain.be
Andrew S RosenAssistant Professor of Chemical & Biological Engineering, Princeton UniversityVerified email at princeton.edu
Michael W GaultoisLeverhulme Research Centre for Functional Materials DesignVerified email at liverpool.ac.uk
T. Wesley SurtaResearch Faculty at Oregon State UniversityVerified email at oregonstate.edu
Yuan ChiangUC Berkeley, Lawrence Berkeley National LaboratoryVerified email at lbl.gov
Elvis HsiehUniversity of California BerkeleyVerified email at berkeley.edu
Matthew J. McDermottNewfound Materials, Inc.Verified email at berkeley.edu

Janosh Riebesell

Radical AI, NYC. prev. Lawrence Berkeley Lab, University of Cambridge

Verified email at lbl.gov - Homepage

materials science machine learning


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CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling B Deng, P Zhong, KJ Jun, J Riebesell, K Han, CJ Bartel, G Ceder Nature Machine Intelligence 5 (9), 1031-1041, 2023	300	2023
A foundation model for atomistic materials chemistry I Batatia, P Benner, Y Chiang, AM Elena, DP Kovács, J Riebesell, ... arXiv preprint arXiv:2401.00096, 2023	151	2023
Matbench Discovery--An evaluation framework for machine learning crystal stability prediction J Riebesell, REA Goodall, A Jain, P Benner, KA Persson, AA Lee arXiv preprint arXiv:2308.14920, 2023	29	2023
A foundation model for atomistic materials chemistry, 2024 I Batatia, P Benner, Y Chiang, AM Elena, DP Kovács, J Riebesell, ... arXiv preprint arXiv:2401.00096, 0	18
Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning B Deng, Y Choi, P Zhong, J Riebesell, S Anand, Z Li, KJ Jun, KA Persson, ... arXiv preprint arXiv:2405.07105, 2024	14	2024
Jobflow: Computational workflows made simple AS Rosen, M Gallant, J George, J Riebesell, H Sahasrabuddhe, JX Shen, ... Journal of Open Source Software 9 (93), 5995, 2024	14	2024
A foundation model for atomistic materials chemistry, arXiv, 2024 I Batatia, P Benner, Y Chiang, AM Elena, DP Kovács, J Riebesell, ... arXiv preprint arXiv:2401.00096 10, 2024	10	2024
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange ML Evans, J Bergsma, A Merkys, CW Andersen, OB Andersson, D Beltrán, ... Digital Discovery 3 (8), 1509-1533, 2024	9	2024
LLaMP: Large language model made powerful for high-fidelity materials knowledge retrieval and distillation Y Chiang, E Hsieh, CH Chou, J Riebesell arXiv preprint arXiv:2401.17244, 2024	8	2024
A foundation model for atomistic materials chemistry, arXiv I Batatia, P Benner, Y Chiang, AM Elena, DP Kovács, J Riebesell, ... arXiv preprint arXiv:2401.00096 10, 2024	6	2024
Crystal Toolkit: A Web App Framework to Improve Usability and Accessibility of Materials Science Research Algorithms M Horton, JX Shen, J Burns, O Cohen, F Chabbey, AM Ganose, R Guha, ... arXiv preprint arXiv:2302.06147, 2023	5	2023
Alpha A Lee. Matbench discovery–an evaluation framework for machine learning crystal stability prediction J Riebesell, REA Goodall, A Jain, P Benner, KA Persson arXiv preprint arXiv:2308.14920, 2023	5	2023
Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning, 2024 B Deng, Y Choi, P Zhong, J Riebesell, S Anand, Z Li, K Jun, KA Persson, ... URL https://arxiv. org/abs/2405.07105.(cited on page 2), 0	5
Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials J Riebesell, T Surta, R Goodall, M Gaultois, AA Lee arXiv preprint arXiv:2401.05848, 2024	3	2024
Discovery of high-performance dielectric materials with machine-learning-guided search J Riebesell, TW Surta, REA Goodall, MW Gaultois Cell Reports Physical Science 5 (10), 2024	1	2024
Systematic softening in universal machine learning interatomic potentials B Deng, Y Choi, P Zhong, J Riebesell, S Anand, Z Li, KJ Jun, KA Persson, ... npj Computational Materials 11 (1), 1-9, 2025		2025
Atomate2: Modular workflows for materials science A Ganose, H Sahasrabuddhe, M Asta, K Beck, T Biswas, A Bonkowski, ... ChemRxiv, 1-66, 2025		2025
Foundational Machine Learning Interatomic Potential to Study Li-Ion Battery Cathode Phase Transformation with Charge Transfer B Deng, P Zhong, KJ Jun, J Riebesell, K Han, CJ Bartel, G Ceder Electrochemical Society Meeting Abstracts prime2024, 333-333, 2024		2024
Towards Machine Learning Foundation Models for Materials Chemistry J Riebesell		2024
Best of Atomistic Machine Learning J Wasmer, J Riebesell, M Evans, B Blaiszik Quanten-Theorie der Materialien, 2023		2023

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