activity
20192024
most citedE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials

1.9k citations · 2.1k across the 11 of their papers we have counts for

collaborators

11 papers

physics.chem-ph2024

Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials

Zachary A. H. Goodwin, Malia B. Wenny, Julia H. Yang +11

Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as ``designer solvents'' a…

cond-mat.mtrl-sci2023★ 1 cited

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 Batzner +4

The properties of lithium metal are key parameters in the design of lithium ion and lithium metal batteries. They are difficult to probe experimentally due to the high reactivity a…

physics.comp-ph2023★ 14 cited

Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size

Albert Musaelian, Anders Johansson, Simon Batzner +1

This work brings the leading accuracy, sample efficiency, and robustness of deep equivariant neural networks to the extreme computational scale. This is achieved through a combinat…

cond-mat.mtrl-sci2023★ 2 cited

Complexity of Many-Body Interactions in Transition Metals via Machine-Learned Force Fields from the TM23 Data Set

Cameron J. Owen, Steven B. Torrisi, Yu Xie +6

This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we co…

physics.comp-ph2022★ 74 cited

Fast Uncertainty Estimates in Deep Learning Interatomic Potentials

Albert Zhu, Simon Batzner, Albert Musaelian +1

Deep learning has emerged as a promising paradigm to give access to highly accurate predictions of molecular and materials properties. A common short-coming shared by current appro…

stat.ML2022★ 103 cited

The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials

Ilyes Batatia, Simon Batzner, Dávid Péter Kovács +6

The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures. Particularly notable among these are the Atomic…