15 citations · 38 across the 6 of their papers we have counts for
7 papers
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.…
Atomistic modeling of uranium monocarbide with a machine learning interatomic potential
Lorena Alzate-Vargas, Kashi N. Subedi, Roxanne M. Tutchton +3
Uranium monocarbide (UC) is an advanced ceramic fuel candidate due to its superior uranium density and thermal conductivity compared to traditional fuels. To accurately model UC at…
Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials
Shriya Gumber, Lorena Alzate-Vargas, Benjamin T. Nebgen +4
Machine learning interatomic potentials (MLIPs) are inherently limited by the accuracy of the training data, usually consisting of energies and forces obtained from quantum mechani…
Toward machine learning interatomic potentials for modeling uranium mononitride
Lorena Alzate-Vargas, Kashi N. Subedi, Nicholas Lubbers +4
Uranium mononitride (UN) is a promising accident-tolerant fuel because of its high fissile density and high thermal conductivity. In this study, we developed the first machine lear…
Molecular heat transport across a time-periodic temperature gradient
Renai Chen, Tammie Gibson, Galen T. Craven
The time-periodic modulation of a temperature gradient can alter the heat transport properties of a physical system. Oscillating thermal gradients give rise to behaviors such as mo…
Data-driven methods for diffusivity prediction in nuclear fuels
Galen T. Craven, Renai Chen, Michael W. D. Cooper +6
The growth rate of structural defects in nuclear fuels under irradiation is intrinsically related to the diffusion rates of the defects in the fuel lattice. The generation and grow…