Alexander V. Shapeev

Materials Chemistry · 2015–2020

A researcher in the Science corpus: 8 papers; trusts 55 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 213 citations into it; #230 of 11,513 by trust here, #4,389 by citations
• Atomic and Molecular Physics, and Optics — 82 citations into it; #218 of 7,135 by trust here, #3,027 by citations
• Computational Theory and Mathematics — 49 citations into it; #4,169 of 12,182 by trust here, #3,033 by citations
• Computational Chemistry & Drug Discovery — 39 citations into it
• Electrical and Electronic Engineering — 35 citations into it
• AI for Physical Sciences — 27 citations into it
• Physical and Theoretical Chemistry — 25 citations into it; #16 of 671 by trust here, #121 by citations
• Molecular Biology — 24 citations into it

Whom Alexander V. Shapeev trusts overall

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

1. J. Behler
2. A. P. Bart'ok
3. Nicholas John Higham
4. Miroslav Kubat
5. Gábor Csányi
6. Zhenwei Li
7. B. Braams
8. M. Finnis
9. Risi Kondor
10. Michele Parrinello

Who trusts Alexander V. Shapeev overall

1. Bohayra Mortazavi
2. Evgeny V. Podryabinkin
3. Chi Chen
4. A. P. Bart'ok
5. An Chen
6. Risi Kondor
7. Ilyes Batatia
8. T. Wen
9. Yuri Mishin
10. Xianglin Liu

Papers from Alexander V. Shapeev's perspective

Ordered by how strongly the researchers Alexander V. Shapeev 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. Accurate Force Field for Molybdenum by Machine Learning Large Materials Data
4. Machine learning based interatomic potential for amorphous carbon
5. High-dimensional neural network potentials for metal surfaces: A prototype study for copper
6. Computational Materials Science X
7. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons.
8. Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
9. Neural Message Passing for Quantum Chemistry (International Conference on Machine Learning)
10. First principles phonon calculations in materials science

Their papers

• Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials (2015) — 1,056 citations
• Active learning of linearly parametrized interatomic potentials (2016) — 626 citations
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials. (2019) — 807 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
• Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials (2018) — 188 citations
• Nanoporous C3N4, C3N5 and C3N6 nanosheets; novel strong semiconductors with low thermal conductivities and appealing optical/electronic properties (2020) — 133 citations
• Exceptional piezoelectricity, high thermal conductivity and stiffness and promising photocatalysis in two-dimensional MoSi2N4 family confirmed by first-principles (2020) — 487 citations