62 citations · 403 across the 25 of their papers we have counts for
6 papers · 1 filter
Machine learning method to determine concentrations of structural defects in irradiated materials
Landon Johnson, Walter Malone, Jason Rizk +4
The formation and subsequent growth of structural defects in an irradiated material can strongly influence the material's performance in technological and industrial applications.…
Neuromorphic heat transport effects in a molecular junction
Renai Chen, Galen T. Craven
Understanding energy transport at the nanoscale is an open and fundamental challenge in the molecular sciences with direct implications for the design of new electronics, computing…
Stoichiometrically-informed symbolic regression for extracting chemical reaction mechanisms from data
Manuel Palma Banos, Joel D. Kress, Rigoberto Hernandez +1
A data-driven computational method is introduced to extract chemical reaction mechanisms from time series chemical concentration data. It is realized through the use of dynamic sym…
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
Sakib Matin, Emily Shinkle, Yulia Pimonova +5
The quality of machine learning interatomic potentials (MLIPs) strongly depends on the quantity of training data as well as the quantum chemistry (QC) level of theory used. Dataset…
Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
Sakib Matin, Alice E. A. Allen, Emily Shinkle +9
Machine learning interatomic potentials (MLIPs) are revolutionizing the field of molecular dynamics (MD) simulations. Recent MLIPs have tended towards more complex architectures tr…
Heat Transport Hysteresis Generated through Frequency Switching of a Time-Dependent Temperature Gradient
Renai Chen, Galen T. Craven
A stochastic energetics framework is applied to examine how periodically shifting the frequency of a time-dependent oscillating temperature gradient affects heat transport in a nan…