Igor Poltavsky

University of Luxembourg — LU

Materials Science · Physical Sciences

15h-index2.7kcitations52works29.62yr avg
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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)

Publications51 total

AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
arXiv (Cornell University)·2025Open Access
Atomic orbits in molecules and materials for improving machine learning force fields
Machine Learning Science and Technology·2025· 1 citedOpen Access
Atomic Orbits in Molecules and Materials for Improving Machine Learning Force Fields
2025Open Access
A practical guide to machine learning interatomic potentials – Status and future
Current Opinion in Solid State and Materials Science·2025· 109 citedOpen Access
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023
Chemical Science·2025· 12 citedOpen Access
Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023
Chemical Science·2025· 10 citedOpen Access
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Model Analysis in the TEA Challenge 2023
2024· 7 cited
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Molecular Dynamics in the TEA Challenge 2023
2024· 5 cited
Accurate Quantum Monte Carlo Forces for Machine-Learned Force Fields: Ethanol as a Benchmark
Journal of Chemical Theory and Computation·2024· 13 citedOpen Access
Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors
Nature Communications·2024· 59 citedOpen Access
Advancing Machine Learning Force Field Designs through Atomic Graph-Based Symmetry Search
2023
Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields under the Microscope
Journal of Chemical Theory and Computation·2023· 9 citedOpen Access
Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields Under the Microscope
arXiv (Cornell University)·2023Open Access
Author Correction: Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
Nature Communications·2023Open Access
Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
Nature Communications·2023· 37 citedOpen Access
Modeling molecular ensembles with gradient-domain machine learning force fields
2023Open Access
Modeling molecular ensembles with gradient-domain machine learning force fields
2023· 2 citedOpen Access
Modeling molecular ensembles with gradient-domain machine learning force fields
Digital Discovery·2023· 15 citedOpen Access
Many-body machine learning models for water, acetonitrile, and methanol
Zenodo (CERN European Organization for Nuclear Research)·2022Open Access
Many-body machine learning models for water, acetonitrile, and methanol
Zenodo (CERN European Organization for Nuclear Research)·2022Open Access
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Frequent Co-authors

Alexandre Tkatchenko(37), Stefan Chmiela(13), T. N. Antsygina(12), K. A. Chishko(12), Valentín Vassilev-Galindo(11), Grégory Fonseca(8), Adil Kabylda(8), Huziel E. Sauceda(6), Klaus‐Robert Müller(6), M. I. Poltavskaya(6), Anton Charkin-Gorbulin(6), Alex M. Maldonado(5), John A. Keith(5), Alastair J. A. Price(5), O. Anatole von Lilienfeld(5), Mirela Puleva(4), Ilyes Batatia(4), Nicholas J. Browning(4), Mengnan Cui(4), J. Thorben Frank(4)