Igor Poltavsky
0000-0002-3188-7017
Publication & Citation Trends
Publications
0 total
aims-PAX: Parallel Active Exploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials
Cited by 0
Semantic Scholar
A practical guide to machine learning interatomic potentials - Status and future
Cited by 147
Semantic Scholar
Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023 OA
Cited by 14
Semantic Scholar
Atomic orbits in molecules and materials for improving machine learning force fields
Cited by 2
Semantic Scholar
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
Cited by 1
Semantic Scholar
Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors OA
Cited by 52
Semantic Scholar
Accurate Quantum Monte Carlo Forces for Machine-Learned Force Fields: Ethanol as a Benchmark OA
Cited by 21
Semantic Scholar
Research Topics
Machine Learning in Materials Science
(29)
Quantum, superfluid, helium dynamics
(14)
Computational Drug Discovery Methods
(13)
Protein Structure and Dynamics
(12)
Advanced Chemical Physics Studies
(8)
Affiliations
National Academy of Sciences of Ukraine
University of Luxembourg
Fritz Haber Institute of the Max Planck Society
B. Verkin Institute for Low Temperature Physics and Engineering of the National Academy of Sciences of Ukraine