| Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design W Chen, AR Tan, AL Ferguson The Journal of chemical physics 149 (7), 2018 | 136 | 2018 |
| Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks D Schwalbe-Koda, AR Tan, R Gómez-Bombarelli Nature communications 12 (1), 5104, 2021 | 79 | 2021 |
| Representations of materials for machine learning J Damewood, J Karaguesian, JR Lunger, AR Tan, M Xie, J Peng, ... Annual Review of Materials Research 53 (1), 399-426, 2023 | 72 | 2023 |
| Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles AR Tan, S Urata, S Goldman, JCB Dietschreit, R Gómez-Bombarelli npj Computational Materials 9 (1), 225, 2023 | 35 | 2023 |
| Suppression of Rayleigh scattering in silica glass by codoping boron and fluorine: molecular dynamics simulations with force-matching and neural network potentials S Urata, N Nakamura, T Tada, AR Tan, R Gómez-Bombarelli, H Hosono The Journal of Physical Chemistry C 126 (4), 2264-2275, 2022 | 16 | 2022 |
| Enhanced sampling of robust molecular datasets with uncertainty-based collective variables AR Tan, JCB Dietschreit, R Gómez-Bombarelli The Journal of Chemical Physics 162 (3), 2025 | 5 | 2025 |
| Modifying ring structures in lithium borate glasses under compression: MD simulations using a machine-learning potential S Urata, AR Tan, R Gómez-Bombarelli Physical Review Materials 8 (3), 033602, 2024 | 5 | 2024 |
| Graph theory-based structural analysis on density anomaly of silica glass AR Tan, S Urata, M Yamada, R Gómez-Bombarelli Computational Materials Science 225, 112190, 2023 | 4* | 2023 |
