976 citations · 1.8k across the 20 of their papers we have counts for
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physics.comp-ph2019★ 976 cited
A Performance and Cost Assessment of Machine Learning Interatomic Potentials
Yunxing Zuo, Chi Chen, Xiangguo Li +8
Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the d…
physics.comp-ph2019★ 70 cited
Data-driven Material Models for Atomistic Simulation
Mitchell A. Wood, Mary Alice Cusentino, Brian D. Wirth +1
The central approximation made in classical molecular dynamics simulation of materials is the interatomic potential used to calculate the forces on the atoms. Great effort and inge…