Katey Huddleston

Materials Chemistry · 2020

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 34 citations into it
• Atomic and Molecular Physics, and Optics — 14 citations into it
• Computational Chemistry & Drug Discovery — 13 citations into it
• AI for Physical Sciences — 7 citations into it

Whom Katey Huddleston trusts overall

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

1. Frank Neese
2. J. Behler

Papers from Katey Huddleston's perspective

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

1. The ORCA program system
2. High-dimensional neural network potentials for metal surfaces: A prototype study for copper
3. Generalized Gradient Approximation Made Simple.
4. Generalized neural-network representation of high-dimensional potential-energy surfaces.
5. Inhomogeneous Electron Gas
6. Comparing molecules and solids across structural and alchemical space.
7. Self-Consistent Equations Including Exchange and Correlation Effects
8. A Chemist's Guide to Density Functional Theory
9. Deep Learning
10. Machine learning based interatomic potential for amorphous carbon

Their papers

• Extending the applicability of the ANI deep learning molecular potential to Sulfur and Halogens. (2020) — 371 citations