Petr Plechac, University of Delaware
Event Date
2025-10-22
Event Time
04:00 pm ~ 05:00 pm
Event Location
617 Wachman Hall
We introduce approximations of ab-initio molecular dynamics derived from quantum mechanics. Molecular dynamics simulations are often used to approximate canonical quantum correlation observables in complex nuclei-electron systems. We present shallow random feature neural networks and provide an analysis of their approximation properties. Furthermore, we describe an adaptive sampling strategy that ensures a near-optimal distribution of features, thus enabling controlled approximation of inter-atomic potentials for molecular dynamics simulations. Finally, we demonstrate that the resulting molecular dynamics accurately approximate correlation observables with quantifiable error estimates.