3 papers
cond-mat.mtrl-sci2026
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…
cond-mat.mtrl-sci2025
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.…
cond-mat.mtrl-sci2025
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…