Evgeny V. Podryabinkin

Materials Chemistry · 2016–2020

A researcher in the Science corpus: 3 papers; trusts 40 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 86 citations into it; #1,742 of 11,513 by trust here, #6,536 by citations
• Atomic and Molecular Physics, and Optics — 38 citations into it
• Computational Theory and Mathematics — 30 citations into it; #4,030 of 12,182 by trust here, #3,033 by citations
• Computational Chemistry & Drug Discovery — 22 citations into it
• Physical and Theoretical Chemistry — 19 citations into it
• AI for Physical Sciences — 17 citations into it
• Molecular Biology — 15 citations into it
• Artificial Intelligence — 8 citations into it

Whom Evgeny V. Podryabinkin trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. J. Behler
2. Alexander V. Shapeev
3. Burr Settles
4. M. Finnis
5. A. P. Bart'ok
6. Gábor Csányi
7. Volker L. Deringer
8. Zhenwei Li
9. J. Perdew
10. Sergei A. Goreinov

Who trusts Evgeny V. Podryabinkin overall

1. Ivan S. Novikov
2. Xianglin Liu
3. A. P. Bart'ok
4. Bohayra Mortazavi
5. Konstantin Gubaev
6. Yuri Mishin
7. Jiaxin Zhang
8. Alexander V. Shapeev
9. James R. Kermode
10. F. Shojaei

Papers from Evgeny V. Podryabinkin's perspective

Ordered by how strongly the researchers Evgeny V. Podryabinkin trusts endorse each paper. Same corpus, a different order for every researcher.

1. Generalized neural-network representation of high-dimensional potential-energy surfaces.
2. Comparing molecules and solids across structural and alchemical space.
3. Fast and accurate modeling of molecular atomization energies with machine learning.
4. Machine learning based interatomic potential for amorphous carbon
5. High-dimensional neural network potentials for metal surfaces: A prototype study for copper
6. Finding Density Functionals with Machine Learning
7. On representing chemical environments
8. Neural Message Passing for Quantum Chemistry (International Conference on Machine Learning)
9. Atom-centered symmetry functions for constructing high-dimensional neural network potentials.
10. Generalized Gradient Approximation Made Simple.

Their papers

• Active learning of linearly parametrized interatomic potentials (2016) — 626 citations
• The MLIP package: moment tensor potentials with MPI and active learning (2020) — 643 citations
• Accelerating high-throughput searches for new alloys with active learning of interatomic potentials (2018) — 329 citations