  Atomistic simulations using accurate energy functions can provide
molecular-level insight into functional motions of molecules in the gas- and in
the condensed phase. Together with recently developed and currently pursued
efforts in integrating and combining this with machine learning techniques
provides a unique opportunity to bring such dynamics simulations closer to
reality. This perspective delineates the present status of the field from
efforts of others in the field and some of your own work and discusses open
questions and future prospects.
