976 citations · 1.8k across the 20 of their papers we have counts for
6 papers · 1 filter
Training Data Selection for Accuracy and Transferability of Interatomic Potentials
David Montes de Oca Zapiain, Mitchell A. Wood, Nicholas Lubbers +3
Advances in machine learning (ML) techniques have enabled the development of interatomic potentials that promise both the accuracy of first principles methods and the low-cost, lin…
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…
Nonlinear Electromagnetic Interactions in Energetic Materials
M. A. Wood, D. A. R. Dalvit, D. S. Moore
We study the scattering of electromagnetic waves in anisotropic energetic materials. Nonlinear light-matter interactions in molecular crystals result in frequency-conversion and po…