So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems T Frank, O Unke, KR Müller Advances in Neural Information Processing Systems 35, 29400-29413, 2022 | 63 | 2022 |
A Euclidean transformer for fast and stable machine learned force fields JT Frank, OT Unke, KR Müller, S Chmiela Nature Communications 15 (1), 6539, 2024 | 17* | 2024 |
Stress and heat flux via automatic differentiation MF Langer, JT Frank, F Knoop The Journal of Chemical Physics 159 (17), 2023 | 13 | 2023 |
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields A Kabylda*, JT Frank*, SS Dou, A Khabibrakhmanov, LM Sandonas, ... | 9 | 2024 |
Crash testing machine learning force fields for molecules, materials, and interfaces: Model analysis in the tea challenge 2023 I Poltavsky, A Charkin-Gorbulin, M Puleva, GC Fonseca, I Batatia, ... | 6 | 2024 |
Learning Neural Network Quantum States with the Linear Method JT Frank, MJ Kastoryano arXiv preprint arXiv:2104.11011, 2021 | 5* | 2021 |
Detect the interactions that matter in matter: Geometric attention for many-body systems T Frank, S Chmiela arXiv preprint arXiv:2106.02549, 2021 | 3 | 2021 |
Euclidean Fast Attention: Machine Learning Global Atomic Representations at Linear Cost JT Frank, S Chmiela, KR Müller, OT Unke arXiv preprint arXiv:2412.08541, 2024 | | 2024 |