  Ab initio Born-Oppenheimer molecular dynamics (AIMD) is a valuable method for
simulating physico-chemical processes of complex systems, including reactive
systems, and for training machine learning models and force fields. Speed and
stability issues on traditional hardware preclude routine AIMD simulations for
larger systems and longer timescales. We postulate that any practically useful
AIMD simulation must generate a trajectory of a minimum 1000 MD steps a day on
a moderate cloud resource. In this work, we implement a computing workflow that
enables routine calculations at this throughput and demonstrate results for
several non-trivial atomistic dynamical systems. In particular, we have
employed the GPU implementation of the Quantum ESPRESSO code which we will show
increases AIMD productivity compared to the CPU version. In order to take
advantage of transient servers (which are more cost and energy effective
compared to the stable servers), we have implemented automatic
restart/continuation of the AIMD runs within the Schr\"odinger Materials
Science Suite. Finally, to reduce simulation size and thus reduce compute time
when modeling surfaces, we have implemented a wall potential constraint. Our
benchmarks using several reactive systems (lithium anode surface/solvent
interface, hydrogen diffusion in an iron grain boundary) show a significant
speed up when running on a GPU-enabled transient server using our updated
implementation.
