Gregor N. C. Simm

Materials Chemistry · 2021–2022

A researcher in the Science corpus: 3 papers; trusts 11 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 67 citations into it
• Computational Theory and Mathematics — 65 citations into it
• Molecular Biology — 53 citations into it
• Computational Chemistry & Drug Discovery — 31 citations into it
• Physical and Theoretical Chemistry — 19 citations into it
• Atomic and Molecular Physics, and Optics — 15 citations into it
• AI for Physical Sciences — 9 citations into it
• Spectroscopy — 7 citations into it

Whom Gregor N. C. Simm trusts overall

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

1. Johannes Klicpera
2. Kristof T. Schütt
3. Alexander V. Shapeev
4. J. Behler
5. Ralf Drautz
6. Risi Kondor
7. A. P. Bart'ok

Papers from Gregor N. C. Simm's perspective

Ordered by how strongly the researchers Gregor N. C. Simm 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. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
4. Directional Message Passing for Molecular Graphs (International Conference on Learning Representations)
5. Cormorant: Covariant Molecular Neural Networks
6. Generalized neural-network representation of high-dimensional potential-energy surfaces.
7. The Graph Neural Network Model
8. Machine learning of accurate energy-conserving molecular force fields
9. Attention is All you Need (Neural Information Processing Systems)
10. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons.

Their papers

• MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields (2022) — 1,193 citations
• The design space of E(3)-equivariant atom-centred interatomic potentials (2022) — 361 citations
• DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design (2021) — 126 citations