2 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-sci2026
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…