49 citations · 85 across the 6 of their papers we have counts for
7 papers
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…
Physical origin of enhanced electrical conduction in aluminum-graphene composites
K. Nepal, C. Ugwumadu, K. N. Subedi +2
The electronic and transport properties of aluminum-graphene composite materials were investigated using ab initio plane wave density functional theory. The interfacial structure i…
Electronic transport in copper-graphene composites
K. N. Subedi, K. Nepal, C. Ugwumadu +2
We investigate the electronic transport properties of copper-graphene composites using a density-functional framework. Conduction in composites by varying the interface distance of…
Structure and charge transport of amorphous -doped : An ab initio study
Rajendra Thapa, Bishal Bhattarai, M. N. Kozicki +2
In this paper, we present ab initio computer models of Cu-doped amorphous Ta2O5 , a promising candidate for Conducting Bridge Random Access Memory (CBRAM) memory devices, and study…
Structural origins of electronic conduction in amorphous copper-doped alumina
K. N. Subedi, K. Prasai, M. N. Kozicki +1
We perform an {\it ab initio} modeling of amorphous copper-doped alumina (a-AlO:Cu), a prospective memory material based on resistance switching, and study the structural o…