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 | 129 | 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 | 57 | 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 | 12 | 2023 |
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 | 7 | 2024 |
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 | 3 | 2024 |
LLaMP: Large language model made powerful for high-fidelity materials knowledge retrieval and distillation Y Chiang, CH Chou, J Riebesell arXiv preprint arXiv:2401.17244, 2024 | 2 | 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, 2024 | 2 | 2024 |
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 | 1 | 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 | 1 | 2023 |
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 PRiME 2024 (October 6-11, 2024), 2024 | | 2024 |
Best of Atomistic Machine Learning J Wasmer, J Riebesell, M Evans, B Blaiszik Quanten-Theorie der Materialien, 2023 | | 2023 |