Kozinsky, Boris

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T. E., & Kozinsky, B. (2022) · Nature Communications

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 2,285

On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

Vandermause, J., Torrisi, S. B., Batzner, S., Xie, Y., Sun, L., Kolpak, A. M., & Kozinsky, B. (2020) · npj Computational Materials

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 459

Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt

Vandermause, J., Xie, Y., Lim, J. S., Owen, C. J., & Kozinsky, B. (2022) · Nature Communications

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 147