Isaac Tamblyn

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 Isaac Tamblyn trusts

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

1. Gábor Csányi (in Materials Chemistry)
2. A. P. Bart'ok (in Materials Chemistry)

Papers from Isaac Tamblyn's perspective

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

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

Their papers

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