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Sergey N. Pozdnyakov
Sergey N. Pozdnyakov
Verified email at epfl.ch
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
1982020
Recursive evaluation and iterative contraction of N-body equivariant features
J Nigam, S Pozdnyakov, M Ceriotti
The Journal of chemical physics 153 (12), 2020
812020
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
372022
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
272021
Smooth, exact rotational symmetrization for deep learning on point clouds
S Pozdnyakov, M Ceriotti
Advances in Neural Information Processing Systems 36, 2024
242024
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
Wigner kernels: body-ordered equivariant machine learning without a basis
F Bigi, SN Pozdnyakov, M Ceriotti
The Journal of Chemical Physics 161 (4), 2024
142024
Completeness of atomic structure representations
J Nigam, SN Pozdnyakov, KK Huguenin-Dumittan, M Ceriotti
APL Machine Learning 2 (1), 2024
142024
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
92022
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
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
62023
Dataset: Randomly-displaced methane configurations
S Pozdnyakov, M Willatt, M Ceriotti
Materials Cloud Archive 2020. 110, 2020
62020
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
Probing the effects of broken symmetries in machine learning
MF Langer, SN Pozdnyakov, M Ceriotti
Machine Learning: Science and Technology 5 (4), 04LT01, 2024
12024
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 1–20