Kyle Mills

Materials Chemistry · 2017

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 — 17 citations into it
• Computational Chemistry & Drug Discovery — 11 citations into it

Whom Kyle Mills trusts overall

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

1. A. P. Bart'ok
2. Matthias Rupp 
3. J. Perdew
4. Alexandre Tkatchenko
5. Sandip De

Papers from Kyle Mills's perspective

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

1. Human-level control through deep reinforcement learning
2. Scikit-learn: Machine Learning in Python
3. Fast and accurate modeling of molecular atomization energies with machine learning.
4. Mastering the game of Go with deep neural networks and tree search
5. Machine learning of accurate energy-conserving molecular force fields
6. Generalized Gradient Approximation Made Simple.
7. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
8. Understanding Machine-learned Density Functionals
9. Comparing molecules and solids across structural and alchemical space.
10. On representing chemical environments

Their papers

• Convolutional neural networks for atomistic systems (2017) — 44 citations