70 citations · 95 across the 4 of their papers we have counts for
4 papers · 1 filter
A physics-informed operator regression framework for extracting data-driven continuum models
Ravi G. Patel, Nathaniel A. Trask, Mitchell A. Wood +1
The application of deep learning toward discovery of data-driven models requires careful application of inductive biases to obtain a description of physics which is both accurate a…
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…
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…
Quantum-Accurate Molecular Dynamics Potential for Tungsten
Mitchell A. Wood, Aidan P. Thompson
The purpose of this short contribution is to report on the development of a Spectral Neighbor Analysis Potential (SNAP) for tungsten. We have focused on the characterization of ela…