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Sergey N. Pozdnyakov
Sergey N. Pozdnyakov
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Title
Cited by
Cited by
Year
Incompleteness of atomic structure representations
SN Pozdnyakov, MJ Willatt, AP Bartók, C Ortner, G Csányi, M Ceriotti
Physical Review Letters 125 (16), 166001, 2020
1772020
Recursive evaluation and iterative contraction of N-body equivariant features
J Nigam, S Pozdnyakov, M Ceriotti
The Journal of chemical physics 153 (12), 2020
752020
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
292022
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
242021
Incompleteness of graph neural networks for points clouds in three dimensions
SN Pozdnyakov, M Ceriotti
Machine Learning: Science and Technology 3 (4), 045020, 2022
23*2022
Local invertibility and sensitivity of atomic structure-feature mappings
SN Pozdnyakov, L Zhang, C Ortner, G Csányi, M Ceriotti
Open Research Europe 1, 2021
152021
Smooth, exact rotational symmetrization for deep learning on point clouds
S Pozdnyakov, M Ceriotti
Advances in Neural Information Processing Systems 36, 2024
142024
Wigner kernels: body-ordered equivariant machine learning without a basis
F Bigi, SN Pozdnyakov, M Ceriotti
arXiv preprint arXiv:2303.04124, 2023
102023
Completeness of atomic structure representations
J Nigam, SN Pozdnyakov, KK Huguenin-Dumittan, M Ceriotti
APL Machine Learning 2 (1), 2024
82024
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
82022
Fast general two-and three-body interatomic potential
S Pozdnyakov, AR Oganov, E Mazhnik, A Mazitov, I Kruglov
arXiv preprint arXiv:1910.07513, 2019
72019
Dataset: Randomly-displaced methane configurations
S Pozdnyakov, M Willatt, M Ceriotti
Materials Cloud Archive 2020. 110, 2020
62020
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
52023
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
22019
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
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Articles 1–19