Lixin Sun

Materials Chemistry · 2019–2022

A researcher in the Science corpus: 3 papers; trusts 26 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 86 citations into it; #11,283 of 11,513 by trust here, #7,154 by citations
• Computational Theory and Mathematics — 36 citations into it
• Atomic and Molecular Physics, and Optics — 32 citations into it
• Computational Chemistry & Drug Discovery — 31 citations into it
• Molecular Biology — 29 citations into it; #17,637 of 17,773 by trust here, #9,476 by citations
• Artificial Intelligence — 20 citations into it; #35,066 of 35,345 by trust here, #13,760 by citations
• AI for Physical Sciences — 19 citations into it
• Model Architectures — 18 citations into it

Whom Lixin Sun trusts overall

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

1. Kristof T. Schütt
2. Risi Kondor
3. Ralf Drautz
4. A. P. Bart'ok
5. Kaiming He
6. Johannes Klicpera
7. Stefan Chmiela
8. Diederik P. Kingma
9. Oliver T. Unke
10. Dan Hendrycks

Who trusts Lixin Sun overall

1. Ilyes Batatia

Papers from Lixin Sun's perspective

Ordered by how strongly the researchers Lixin Sun 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. ImageNet Large Scale Visual Recognition Challenge
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials (2021) — 2,192 citations
• Learning local equivariant representations for large-scale atomistic dynamics (2022) — 820 citations
• On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events (2019) — 424 citations