| Incompleteness of atomic structure representations SN Pozdnyakov, MJ Willatt, AP Bartók, C Ortner, G Csányi, M Ceriotti Physical Review Letters 125 (16), 166001, 2020 | 196 | 2020 |
| Recursive evaluation and iterative contraction of N-body equivariant features J Nigam, S Pozdnyakov, M Ceriotti The Journal of chemical physics 153 (12), 2020 | 81 | 2020 |
| 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 | 37 | 2022 |
| Incompleteness of graph neural networks for points clouds in three dimensions SN Pozdnyakov, M Ceriotti Machine Learning: Science and Technology 3 (4), 045020, 2022 | 29* | 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 | 26 | 2021 |
| Smooth, exact rotational symmetrization for deep learning on point clouds S Pozdnyakov, M Ceriotti Advances in Neural Information Processing Systems 36, 2024 | 24 | 2024 |
| Completeness of atomic structure representations J Nigam, SN Pozdnyakov, KK Huguenin-Dumittan, M Ceriotti APL Machine Learning 2 (1), 2024 | 15 | 2024 |
| Local invertibility and sensitivity of atomic structure-feature mappings SN Pozdnyakov, L Zhang, C Ortner, G Csányi, M Ceriotti Open Research Europe 1, 2021 | 15 | 2021 |
| Wigner kernels: body-ordered equivariant machine learning without a basis F Bigi, SN Pozdnyakov, M Ceriotti The Journal of Chemical Physics 161 (4), 2024 | 14 | 2024 |
| Comment on “Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions”[J. Chem. Phys. 156, 034302 (2022)] SN Pozdnyakov, MJ Willatt, AP Bartók, C Ortner, G Csányi, M Ceriotti The Journal of Chemical Physics 157 (17), 2022 | 9 | 2022 |
| Fast general two-and three-body interatomic potential S Pozdnyakov, AR Oganov, E Mazhnik, A Mazitov, I Kruglov arXiv preprint arXiv:1910.07513, 2019 | 7 | 2019 |
| Fast general two-and three-body interatomic potential S Pozdnyakov, AR Oganov, E Mazhnik, A Mazitov, I Kruglov Physical Review B 107 (12), 125160, 2023 | 6 | 2023 |
| Dataset: Randomly-displaced methane configurations S Pozdnyakov, M Willatt, M Ceriotti Materials Cloud Archive 2020. 110, 2020 | 6 | 2020 |
| Machine learning interatomic potentials for global optimization and molecular dynamics simulation IA Kruglov, PE Dolgirev, AR Oganov, AB Mazitov, SN Pozdnyakov, ... Materials Informatics: Methods, Tools and Applications, 253-288, 2019 | 2 | 2019 |
| Probing the effects of broken symmetries in machine learning MF Langer, SN Pozdnyakov, M Ceriotti Machine Learning: Science and Technology 5 (4), 04LT01, 2024 | 1 | 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 |
| Local invertibility and sensitivity of atomic structure-feature mappings. L Zhang, G Csányi, SN Pozdnyakov, C Ortner, M Ceriotti | | 2021 |
| MACHINE LEARNING POTENTIAL S Pozdnyakov, E Mazhnik, I Kruglov, A Oganov, A Yanilkin 3rd Kazan Summer School on Chemoinformatics, 35-35, 2017 | | 2017 |
| Group ID U12743 A Anelli, E Baldi, B Mahmoud, F Chiheb Bigi, M Ceriotti, R Cersonsky, ... | | |
| MACHINE LEARNING POTENTIAL A Oganov, E Mazhnik, I Kruglov, S Pozdnyakov, A Yanilkin | | |