2 papers
physics.chem-ph2025
Development of machine-learned interatomic potentials to predict structure, transport, and reactivity in platinum-based fuel cells
Kamron Fazel, Sam Brown, Jacob Clary +5
Machine-learned interatomic potentials (MLIPs) have rapidly progressed in accuracy, speed, and data efficiency in recent years. However, training robust MLIPs in multicomponent sys…
cond-mat.soft2017
Molecular simulations of entangled defect structures around nanoparticles in nematic liquid crystals
Anja Humpert, Samuel F. Brown, Michael P. Allen
We investigate the defect structures forming around two nanoparticles in a Gay-Berne nematic liquid crystal using molecular simulations. For small separations, disclinations entang…