| Completeness of atomic structure representations J Nigam, SN Pozdnyakov, KK Huguenin-Dumittan, M Ceriotti APL Machine Learning 2 (1), 2024 | 17 | 2024 |
| Physics-inspired equivariant descriptors of nonbonded interactions KK Huguenin-Dumittan, P Loche, N Haoran, M Ceriotti The Journal of Physical Chemistry Letters 14 (43), 9612-9618, 2023 | 15 | 2023 |
| A smooth basis for atomistic machine learning F Bigi, KK Huguenin-Dumittan, M Ceriotti, DE Manolopoulos The Journal of Chemical Physics 157 (23), 2022 | 8 | 2022 |
| Fast and flexible range-separated models for atomistic machine learning P Loche, KK Huguenin-Dumittan, M Honarmand, Q Xu, E Rumiantsev, ... arXiv preprint arXiv:2412.03281, 2024 | 2 | 2024 |
| Adaptive energy reference for machine-learning models of the electronic density of states WB How, S Chong, F Grasselli, KK Huguenin-Dumittan, M Ceriotti arXiv preprint arXiv:2407.01068, 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 |
| 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 |
| Group ID U12743 A Anelli, E Baldi, B Mahmoud, F Chiheb Bigi, M Ceriotti, R Cersonsky, ... | | |
