Simon L. Batzner

Materials Chemistry · 2019–2023

A researcher in the Science corpus: 5 papers; trusts 31 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 136 citations into it; #11,285 of 11,513 by trust here, #6,876 by citations
• Computational Theory and Mathematics — 53 citations into it
• Atomic and Molecular Physics, and Optics — 43 citations into it
• Computational Chemistry & Drug Discovery — 42 citations into it
• Molecular Biology — 38 citations into it; #17,645 of 17,773 by trust here, #9,476 by citations
• Artificial Intelligence — 37 citations into it; #35,071 of 35,345 by trust here, #13,760 by citations
• Physical and Theoretical Chemistry — 29 citations into it
• Model Architectures — 26 citations into it

Whom Simon L. Batzner trusts overall

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

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

Who trusts Simon L. Batzner overall

1. Ilyes Batatia
2. Mario Geiger
3. Kenneth Atz
4. Kristof T. Schütt
5. Bowen Jing
6. Tess E. Smidt
7. Hannes Stärk
8. Amil Merchant
9. D. Kov'acs
10. Oliver T. Unke

Papers from Simon L. Batzner's perspective

Ordered by how strongly the researchers Simon L. Batzner 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. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

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
• Scaling deep learning for materials discovery (2023) — 1,483 citations
• The design space of E(3)-equivariant atom-centred interatomic potentials (2022) — 361 citations
• On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events (2019) — 424 citations