976 citations · 1.3k across the 4 of their papers we have counts for
4 papers
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…
An Electrostatic Spectral Neighbor Analysis Potential (eSNAP) for Lithium Nitride
Zhi Deng, Chi Chen, Xiang-Guo Li +1
Machine-learned interatomic potentials based on local environment descriptors represent a transformative leap over traditional potentials based on rigid functional forms in terms o…
Quantum-Accurate Spectral Neighbor Analysis Potential Models for Ni-Mo Binary Alloys and FCC Metals
Xiang-Guo Li, Chongze Hu, Chi Chen +3
In recent years, efficient inter-atomic potentials approaching the accuracy of density functional theory (DFT) calculations have been developed using rigorous atomic descriptors sa…
Accurate Force Field for Molybdenum by Machine Learning Large Materials Data
Chi Chen, Zhi Deng, Richard Tran +3
In this work, we present a highly accurate spectral neighbor analysis potential (SNAP) model for molybdenum (Mo) developed through the rigorous application of machine learning tech…