| Roadmap on machine learning in electronic structure HJ Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ... Electronic Structure 4 (2), 023004, 2022 | 149 | 2022 |
| Recursive evaluation and iterative contraction of N-body equivariant features J Nigam, S Pozdnyakov, M Ceriotti The Journal of chemical physics 153 (12), 2020 | 84 | 2020 |
| Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties J Nigam, MJ Willatt, M Ceriotti The Journal of Chemical Physics 156 (1), 2022 | 64 | 2022 |
| Multi-scale approach for the prediction of atomic scale properties A Grisafi, J Nigam, M Ceriotti Chemical science 12 (6), 2078-2090, 2021 | 61 | 2021 |
| Unified theory of atom-centered representations and message-passing machine-learning schemes J Nigam, S Pozdnyakov, G Fraux, M Ceriotti The Journal of Chemical Physics 156 (20), 2022 | 39 | 2022 |
| Optimal radial basis for density-based atomic representations A Goscinski, F Musil, S Pozdnyakov, J Nigam, M Ceriotti The Journal of Chemical Physics 155 (10), 2021 | 28 | 2021 |
| Completeness of atomic structure representations J Nigam, SN Pozdnyakov, KK Huguenin-Dumittan, M Ceriotti APL Machine Learning 2 (1), 2024 | 17 | 2024 |
| Electronic Excited States from Physically Constrained Machine Learning E Cignoni, D Suman, J Nigam, L Cupellini, B Mennucci, M Ceriotti ACS Central Science 10 (3), 637-648, 2024 | 11 | 2024 |
| Characterization of Test Mass Scattering J Nigam, G Venugopalan, J Eichholz, RX Adhikari LIGO-SURF Report, 2017 | 2 | 2017 |
| Scalable emulation of protein equilibrium ensembles with generative deep learning S Lewis, T Hempel, J Jiménez Luna, M Gastegger, Y Xie, AYK Foong, ... bioRxiv, 2024.12. 05.626885, 2024 | 1 | 2024 |
| Expanding density-correlation machine learning representations for anisotropic coarse-grained particles A Lin, KK Huguenin-Dumittan, YC Cho, J Nigam, RK Cersonsky The Journal of chemical physics 161 (7), 2024 | | 2024 |
| Integrating symmetry and physical constraints into atomic-scale machine learning J Nigam EPFL, 2024 | | 2024 |
| Completeness of representations in atomistic machine learning J Nigam, M Ceriotti, S Pozdnyakov, K Huguenin-Dumittan Bulletin of the American Physical Society, 2024 | | 2024 |
| Anisotropic Machine Learning Representations for Multiscale Systems A Lin, K Huguenin-Dumittan, J Nigam, YC Cho, R Cersonsky Bulletin of the American Physical Society, 2024 | | 2024 |
| Unifying atom-centered descriptions and message-passing machine learning schemes J Nigam, M Ceriotti APS March Meeting Abstracts 2023, D17. 009, 2023 | | 2023 |
| Section 2.6-Integrated machine learning models: electronic structure accuracy beyond local potentials M Veit, A Grisafi, J Nigam, M Ceriotti Roadmap on Machine Learning in Electronic Structure, 47, 0 | | |
Michele CeriottiAssociate Professor at EPFL, Institute of MaterialsEmail verificata su epfl.ch