| How to validate machine-learned interatomic potentials JD Morrow, JLA Gardner, VL Deringer The Journal of chemical physics 158 (12), 2023 | 82 | 2023 |
| Robustness of local predictions in atomistic machine learning models S Chong, F Grasselli, C Ben Mahmoud, JD Morrow, VL Deringer, ... Journal of Chemical Theory and Computation 19 (22), 8020-8031, 2023 | 21 | 2023 |
| Indirect learning and physically guided validation of interatomic potential models JD Morrow, VL Deringer The Journal of Chemical Physics 157 (10), 2022 | 14 | 2022 |
| Machine-learned acceleration for molecular dynamics in CASTEP TK Stenczel, Z El-Machachi, G Liepuoniute, JD Morrow, AP Bartók, ... The Journal of Chemical Physics 159 (4), 2023 | 12 | 2023 |
| Understanding Defects in Amorphous Silicon with Million‐Atom Simulations and Machine Learning JD Morrow, C Ugwumadu, DA Drabold, SR Elliott, AL Goodwin, ... Angewandte Chemie 136 (22), e202403842, 2024 | 6 | 2024 |
| Indirect Learning of Interatomic Potentials for Accelerated Materials Simulations JD Morrow, VL Deringer arXiv preprint arXiv:2111.11120, 2021 | 2 | 2021 |
| An automated framework for exploring and learning potential-energy surfaces Y Liu, JD Morrow, C Ertural, NL Fragapane, JLA Gardner, AA Naik, ... arXiv preprint arXiv:2412.16736, 2024 | 1 | 2024 |
Volker L. DeringerUniversity of OxfordPatvirtintas el. paštas chem.ox.ac.uk