Chi Chen

Materials Chemistry · npj Computational Materials · 2017–2022

A researcher in the Science corpus: 10 papers; trusts 38 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 414 citations into it; #3,579 of 11,513 by trust here, #5,094 by citations
• Computational Theory and Mathematics — 119 citations into it; #6,591 of 12,182 by trust here, #2,689 by citations
• Atomic and Molecular Physics, and Optics — 103 citations into it
• Computational Chemistry & Drug Discovery — 92 citations into it; #421 of 425 by trust here, #266 by citations
• Molecular Biology — 65 citations into it
• Artificial Intelligence — 49 citations into it
• Physical and Theoretical Chemistry — 43 citations into it
• AI for Physical Sciences — 42 citations into it

Whom Chi Chen trusts overall

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

1. Alexander V. Shapeev
2. J. Behler
3. A. P. Bart'ok
4. Stefan Chmiela
5. Geoffrey E. Hinton
6. Shyue Ping Ong
7. Anubhav Jain
8. Yann LeCun
9. Kamal Choudhary
10. Oriol Vinyals

Who trusts Chi Chen overall

1. Tian Xie
2. Yuri Mishin
3. Xianglin Liu
4. Amil Merchant
5. Xiang Fu
6. Shyue Ping Ong
7. Johannes Klicpera
8. Fan-Yun Sun
9. A. P. Bart'ok
10. Simon L. Batzner

Papers from Chi Chen's perspective

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

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

Their papers

• Accurate Force Field for Molybdenum by Machine Learning Large Materials Data (2017) — 129 citations
• Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals (2018) — 1,359 citations
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials. (2019) — 807 citations
• A universal graph deep learning interatomic potential for the periodic table (2022) — 1,004 citations
• Probing Solid–Solid Interfacial Reactions in All-Solid-State Sodium-Ion Batteries with First-Principles Calculations (2018) — 180 citations
• Deep neural networks for accurate predictions of crystal stability (2017) — 251 citations
• Recent advances and applications of deep learning methods in materials science (2021) — 995 citations
• A Critical Review of Machine Learning of Energy Materials (2020) — 478 citations
• Grain boundary properties of elemental metals (2019) — 238 citations
• Complex strengthening mechanisms in the NbMoTaW multi-principal element alloy (2019) — 239 citations