14 citations · 22 across the 3 of their papers we have counts for
4 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.…
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…
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…
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…