70 citations · 95 across the 4 of their papers we have counts for
7 papers
Efficacy of the Radial Pair Potential Approximation for Molecular Dynamics Simulations of Dense Plasmas
Lucas J. Stanek, Raymond C. Clay, M. W. C. Dharma-wardana +3
Macroscopic simulations of dense plasmas rely on detailed microscopic information that can be computationally expensive and is difficult to verify experimentally. In this work, we…
Thermodynamically consistent physics-informed neural networks for hyperbolic systems
Ravi G. Patel, Indu Manickam, Nathaniel A. Trask +4
Physics-informed neural network architectures have emerged as a powerful tool for developing flexible PDE solvers which easily assimilate data, but face challenges related to the P…
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…
Explicit Multi-element Extension of the Spectral Neighbor Analysis Potential for Chemically Complex Systems
Mary Alice Cusentino, Mitchell A. Wood, Aidan P. Thompson
A natural extension of the descriptors used in the Spectral Neighbor Analysis Potential (SNAP) method is derived to treat atomic interactions in chemically complex systems. Atomic…
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…