Justin S. Smith

Materials Chemistry · 2016–2020

A researcher in the Science corpus: 7 papers; trusts 15 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 158 citations into it; #1,230 of 11,513 by trust here, #2,666 by citations
• Computational Theory and Mathematics — 96 citations into it; #3,300 of 12,182 by trust here, #420 by citations
• Atomic and Molecular Physics, and Optics — 86 citations into it; #1,772 of 7,135 by trust here, #1,149 by citations
• Molecular Biology — 76 citations into it
• Computational Chemistry & Drug Discovery — 72 citations into it; #216 of 425 by trust here, #122 by citations
• Physical and Theoretical Chemistry — 58 citations into it
• AI for Physical Sciences — 34 citations into it; #136 of 247 by trust here, #49 by citations
• Spectroscopy — 22 citations into it

Whom Justin S. Smith trusts overall

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

1. Frank Neese
2. J. Behler
3. Sinno Jialin Pan
4. Matthew E. Taylor
5. Qiang Yang
6. Jeng-Da Chai
7. Matthias Rupp 
8. M. Head‐Gordon
9. Christoph Riplinger
10. Peter Stone

Who trusts Justin S. Smith overall

1. Kyle Swanson
2. Wenhao Gao
3. Frank No'e
4. Garrett B. Goh
5. Albert Musaelian
6. Pascal Friederich
7. Brandon M. Anderson
8. Raphael J. L. Townshend
9. Simon L. Batzner
10. José Jiménez-Luna

Papers from Justin S. Smith's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost (2016) — 1,346 citations
• Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning (2019) — 591 citations
• Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks. (2018) — 105 citations
• Hierarchical modeling of molecular energies using a deep neural network. (2017) — 312 citations
• Less is more: sampling chemical space with active learning (2018) — 750 citations
• Extending the applicability of the ANI deep learning molecular potential to Sulfur and Halogens. (2020) — 371 citations
• Outsmarting Quantum Chemistry Through Transfer Learning (2018)