Yu-Hang Tang

Materials Chemistry · 2017

A researcher in the Science corpus: 1 paper; trusts 15 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 10 citations into it
• Computational Chemistry & Drug Discovery — 7 citations into it
• Atomic and Molecular Physics, and Optics — 6 citations into it
• Computational Theory and Mathematics — 6 citations into it
• Molecular Biology — 5 citations into it

Whom Yu-Hang Tang trusts overall

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

1. A. P. Bart'ok
2. V. Botu
3. Alireza Khorshidi
4. Stefan Chmiela
5. W. L. Jorgensen
6. Grégoire Ferré
7. Kristof T. Schütt
8. Risi Kondor
9. Ronald R. Coifman
10. J. Behler

Papers from Yu-Hang Tang's perspective

Ordered by how strongly the researchers Yu-Hang Tang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Machine learning of accurate energy-conserving molecular force fields
2. Two-Point Step Size Gradient Methods
3. On representing chemical environments
4. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons.
5. Learning scheme to predict atomic forces and accelerate materials simulations
6. Atom-centered symmetry functions for constructing high-dimensional neural network potentials.
7. Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces.
8. Quantum-chemical insights from deep tensor neural networks
9. Mature HIV-1 capsid structure by cryo-electron microscopy and all-atom molecular dynamics (Nature)
10. UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations

Their papers

• An Atomistic Fingerprint Algorithm for Learning Ab Initio Molecular Force Fields (2017) — 31 citations