Differentiable Molecular Simulations

Molecular dynamics simulations use statistical mechanics at the atomistic scale to enable the elucidation of fundamental mechanisms and the engineering of matter for desired tasks. The behavior of molecular systems at the microscale is simulated with differential equations parameterized by a Hamiltonian. In order to derive predictive microscopic models, one wishes to infer a molecular Hamiltonian that agrees with observed macroscopic quantities. From the perspective of engineering, one wishes to control the Hamiltonian to achieve desired simulation outcomes and structures, as in optical control, to realize systems with the desired Hamiltonian in the lab. We demonstrate how these tasks can be achieved using simulations with efficient automatic differentiation where simulation outcomes can be analytically differentiated with respect to Hamiltonians, opening up new routes for parameterizing Hamiltonians to infer macroscopic models and develop control protocols. The applications we present including solving inverse structure elucidation problem from experimental observation and parameterizing control protocols for non-equilibrium chemical dynamics.