Sun, Lixin

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

A generative model for inorganic materials design

Zeni, C., Pinsler, R., Zügner, D., Fowler, A., Horton, M., Fu, X., Wang, Z., Shysheya, A., Crabbé, J., Ueda, S., et al. (2025) · Nature

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 518

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