Physical Sciences and Mathematics Commons^™

Developments Of Machine Learning Potentials For Atomistic Simulations, Howard Yanxon

UNLV Theses, Dissertations, Professional Papers, and Capstones

Atomistic modeling methods such as molecular dynamics play important roles in investigating time-dependent physical and chemical processes at the microscopic level. In the simulations, energy and forces, sometimes including stress tensor, need to be recalculated iteratively as the atomic configuration evolves. Consequently, atomistic simulations crucially depend on the accuracy of the underlying potential energy surface. Modern quantum mechanical modeling based on density functional theory can consistently generate an accurate description of the potential energy surface. In most cases, molecular dynamics simulations based on density functional theory suffer from highly demanding computational costs. On the other hand, atomistic simulations based on …