Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems. - Jonathan P. Mailoa; Mordechai Kornbluth; Simon L. Batzner; Georgy Samsonidze; Stephen T. Lam; Chris Ablitt; Nicola Molinari; Boris Kozinsky (CoRR, 2019)

A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems. - Jonathan P. Mailoa; Mordechai Kornbluth; Simon L. Batzner; Georgy Samsonidze; Stephen T. Lam; Jonathan Vandermause; Chris Ablitt; Nicola Molinari; Boris Kozinsky (Nat. Mach. Intell., 2019)

Multitask machine learning of collective variables for enhanced sampling of rare events. - Lixin Sun; Jonathan Vandermause; Simon L. Batzner; Yu Xie; David Clark; Wei Chen; Boris Kozinsky (CoRR, 2020)

SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials. - Simon L. Batzner; Tess E. Smidt; Lixin Sun; Jonathan P. Mailoa; Mordechai Kornbluth; Nicola Molinari; Boris Kozinsky (CoRR, 2021)

Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics. - Albert Musaelian; Simon L. Batzner; Anders Johansson; Lixin Sun; Cameron J. Owen; Mordechai Kornbluth; Boris Kozinsky (CoRR, 2022)

The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials. - Ilyes Batatia; Simon L. Batzner; Dávid Péter Kovács; Albert Musaelian; Gregor N. C. Simm; Ralf Drautz; Christoph Ortner; Boris Kozinsky; Gábor Csányi (CoRR, 2022)

Fast Uncertainty Estimates in Deep Learning Interatomic Potentials. - Albert Zhu; Simon L. Batzner; Albert Musaelian; Boris Kozinsky (CoRR, 2022)

Scaling the Leading Accuracy of Deep Equivariant Models to Biomolecular Simulations of Realistic Size. - Boris Kozinsky; Albert Musaelian; Anders Johansson; Simon L. Batzner (SC, 2023)

Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials. - Mgcini Keith Phuthi; Archie Mingze Yao; Simon L. Batzner; Albert Musaelian; Boris Kozinsky; Ekin Dogus Cubuk; Venkatasubramanian Viswanathan (CoRR, 2023)

Predicting emergence of crystals from amorphous matter with deep learning. - Muratahan Aykol; Amil Merchant; Simon L. Batzner; Jennifer N. Wei; Ekin Dogus Cubuk (CoRR, 2023)

Scaling deep learning for materials discovery. - Amil Merchant; Simon L. Batzner; Samuel S. Schoenholz; Muratahan Aykol; Gowoon Cheon; Ekin Dogus Cubuk (Nat., 2023)

Biasing energy surfaces towards the unknown. - Simon L. Batzner (Nat. Comput. Sci., 2023)

Generative Hierarchical Materials Search. - Sherry Yang 001; Simon L. Batzner; Ruiqi Gao; Muratahan Aykol; Alexander L. Gaunt; Brendan McMorrow; Danilo Jimenez Rezende; Dale Schuurmans; Igor Mordatch; Ekin Dogus Cubuk (NeurIPS, 2024)

The design space of E(3)-equivariant atom-centred interatomic potentials. - Ilyes Batatia; Simon L. Batzner; Dávid Péter Kovács; Albert Musaelian; Gregor N. C. Simm; Ralf Drautz; Christoph Ortner; Boris Kozinsky; Gábor Csányi (Nat. Mac. Intell., 2025)

Predicting emergence of crystals from amorphous precursors with deep learning potentials. - Muratahan Aykol; Amil Merchant; Simon L. Batzner; Jennifer N. Wei; Ekin Dogus Cubuk (Nat. Comput. Sci., 2025)