James R. Kermode

Materials Chemistry · 2008–2018

A researcher in the Science corpus: 8 papers; trusts 21 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 128 citations into it; #1,678 of 11,513 by trust here, #2,304 by citations
• Atomic and Molecular Physics, and Optics — 82 citations into it; #2,428 of 7,135 by trust here, #2,081 by citations
• Computational Theory and Mathematics — 44 citations into it; #7,377 of 12,182 by trust here, #1,233 by citations
• Computational Chemistry & Drug Discovery — 38 citations into it
• Physical and Theoretical Chemistry — 31 citations into it
• Electrical and Electronic Engineering — 24 citations into it
• Geophysics — 15 citations into it
• Artificial Intelligence — 7 citations into it

Whom James R. Kermode trusts overall

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

1. A. P. Bart'ok
2. Gábor Csányi
3. Peter Gumbsch
4. Alexander V. Shapeev
5. J. Behler
6. Volker L. Deringer
7. Markus J. Buehler
8. M. Finnis
9. Kevin P. Murphy
10. S. Plimpton

Who trusts James R. Kermode overall

1. Alexander V. Shapeev
2. A. P. Bart'ok
3. Sam Schoenholz
4. Evgeny V. Podryabinkin
5. Xianglin Liu
6. G. P. P. Pun
7. Yuri Mishin
8. Gábor Csányi
9. Noam Bernstein
10. Rohit Batra

Papers from James R. Kermode's perspective

Ordered by how strongly the researchers James R. Kermode 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. High-dimensional neural network potentials for metal surfaces: A prototype study for copper
5. Machine learning based interatomic potential for amorphous carbon
6. Fast parallel algorithms for short-range molecular dynamics
7. Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
8. First principles phonon calculations in materials science
9. Multiparadigm modeling of dynamical crack propagation in silicon using a reactive force field.
10. Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

Their papers

• Machine learning unifies the modeling of materials and molecules (2017) — 652 citations
• Machine Learning a General-Purpose Interatomic Potential for Silicon (2018) — 457 citations
• Low-speed fracture instabilities in a brittle crystal (2008) — 204 citations
• Atomistic aspects of fracture (2015) — 161 citations
• Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces. (2015) — 514 citations
• The atomic simulation environment-a Python library for working with atoms. (2017) — 3,264 citations
• Hybrid atomistic simulation methods for materials systems (2009) — 165 citations
• Supplementary Materials for Machine learning unifies the modeling of materials and molecules (2017)