Tess E. Smidt

Materials Chemistry · 2016–2023

A researcher in the Science corpus: 9 papers; trusts 191 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 171 citations into it; #9,624 of 11,513 by trust here, #5,878 by citations
• Artificial Intelligence — 94 citations into it; #35,300 of 35,345 by trust here, #15,866 by citations
• Computational Theory and Mathematics — 89 citations into it; #11,870 of 12,182 by trust here, #6,798 by citations
• Model Architectures — 72 citations into it; #962 of 2,278 by trust here, #749 by citations
• Computational Chemistry & Drug Discovery — 66 citations into it; #405 of 425 by trust here, #356 by citations
• Molecular Biology — 64 citations into it
• Computer Vision and Pattern Recognition — 60 citations into it; #9,823 of 21,575 by trust here, #6,281 by citations
• Atomic and Molecular Physics, and Optics — 49 citations into it

Whom Tess E. Smidt trusts overall

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

1. Johannes Klicpera
2. Max Welling
3. Geoffrey E. Hinton
4. Taco Cohen
5. Kristof T. Schütt
6. Risi Kondor
7. Diederik P. Kingma
8. Alex Krizhevsky
9. Yoshua Bengio
10. Kaiming He

Who trusts Tess E. Smidt overall

1. Bowen Jing
2. David W. Romero
3. Mario Geiger
4. Raphael J. L. Townshend
5. Petar Velickovic
6. Yi Liao
7. Risi Kondor
8. Amil Merchant
9. Gabriele Corso
10. Hannes Stärk

Papers from Tess E. Smidt's perspective

Ordered by how strongly the researchers Tess E. Smidt 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds (2018) — 1,225 citations
• E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials (2021) — 2,192 citations
• e3nn: Euclidean Neural Networks (2022) — 301 citations
• Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties (2020) — 85 citations
• Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows (2017) — 329 citations
• SE(3)-equivariant prediction of molecular wavefunctions and electronic densities (2021) — 127 citations
• EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations (2023) — 359 citations
• Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs (2022) — 407 citations
• Design and construction of the MicroBooNE detector (2016) — 392 citations