Noam Bernstein

Materials Chemistry · 2004–2021

A researcher in the Science corpus: 9 papers; trusts 21 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 197 citations into it; #2,174 of 11,513 by trust here, #3,574 by citations
• Atomic and Molecular Physics, and Optics — 74 citations into it
• Computational Theory and Mathematics — 65 citations into it; #7,374 of 12,182 by trust here, #1,133 by citations
• Computational Chemistry & Drug Discovery — 43 citations into it
• Physical and Theoretical Chemistry — 38 citations into it
• Electrical and Electronic Engineering — 32 citations into it
• Biomedical Engineering — 18 citations into it
• Geophysics — 10 citations into it

Whom Noam Bernstein trusts overall

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

1. Ellad B. Tadmor
2. A. P. Bart'ok
3. J. Behler
4. Gábor Csányi
5. S. Hai
6. Alexander V. Shapeev
7. Volker L. Deringer
8. R. Khare
9. S. Plimpton
10. Frank H. Stillinger

Who trusts Noam Bernstein overall

1. Harold S. Park
2. Piyas Chowdhury
3. Sam Schoenholz
4. Ken Gall
5. Xianglin Liu
6. Donald J. Siegel
7. H. Sehitoglu
8. Ellad B. Tadmor
9. James R. Kermode

Papers from Noam Bernstein's perspective

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

1. Generalized neural-network representation of high-dimensional potential-energy surfaces.
2. Machine learning based interatomic potential for amorphous carbon
3. Comparing molecules and solids across structural and alchemical space.
4. Accurate Force Field for Molybdenum by Machine Learning Large Materials Data
5. High-dimensional neural network potentials for metal surfaces: A prototype study for copper
6. On representing chemical environments
7. Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
8. Fast parallel algorithms for short-range molecular dynamics
9. Empirical interatomic potential for silicon with improved elastic properties.
10. First principles phonon calculations in materials science

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
• Gaussian Process Regression for Materials and Molecules (2021) — 1,025 citations
• A first-principles measure for the twinnability of FCC metals (2004) — 265 citations
• Tight-binding calculations of stacking energies and twinnability in fcc metals (2004) — 212 citations
• Hybrid atomistic simulation methods for materials systems (2009) — 165 citations
• Adsorption-induced deformation of nanoporous materials—A review (2017) — 253 citations
• Bright triplet excitons in caesium lead halide perovskites (2017) — 922 citations
• What superconducts in sulfur hydrides under pressure and why (2014) — 166 citations