8 citations · 12 across the 4 of their papers we have counts for
4 papers
Benchmarking phonon anharmonicity in machine learning interatomic potentials
Sasaank Bandi, Chao Jiang, Chris A. Marianetti
Machine learning approaches have recently emerged as powerful tools to probe structure-property relationships in crystals and molecules. Specifically, Machine learning interatomic…
Precisely computing phonons via irreducible derivatives
Sasaank Bandi, C. A. Marianetti
Computing phonons from first-principles is typically considered a solved problem, yet inadequacies in existing techniques continue to yield deficient results in systems with sensit…
Parameterizing empirical interatomic potentials for predicting thermophysical properties via an irreducible derivative approach: the case of ThO and UO
Shuxiang Zhou, Chao Jiang, Enda Xiao +6
The accuracy of classical physical property predictions using molecular dynamics simulations is determined by the quality of the interatomic potentials. Here we introduce a trainin…
Validation of non-negative matrix factorization for assessment of atomic pair-distribution function (PDF) data in a real-time streaming context
Chia-Hao Liu, Christopher J. Wright, Ran Gu +9
We validate the use of matrix factorization for the automatic identification of relevant components from atomic pair distribution function (PDF) data. We also present a newly devel…