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
Showing cond-mat.mtrl-sciShow all

2 papers · 1 filter

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…

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…