Deep Learning Unravels a Dynamic Hierarchy While Empowering molecular Dynamics Simulations

Abstract

Molecular dynamics (MD) provide predictive understanding of the behavior of condensed matter. However, its true potential remains largely untested because relevant timescales are often inaccessible, limited portions of conformation space get sampled, and infrequent events are usually irreproducible. A culprit is the huge informational burden required to iterate integration steps. To address the problem, deep learning is applied to encode the dynamics into a shorthand embodiment retaining only essential topological features of the vector field that steers MD integration. The flow is simplified via an equivalence relation that identifies conformations within basins of attraction in potential energy and encodes the dynamics onto a modulo-basin ?quotient space? where fast motions are averaged out. The quotient space projection enables coverage of realistic timescales while unraveling the underlying dynamic hierarchy. Deep learning is exploited to propagate the simplified trajectory beyond MD-accessible timescales and to reconstruct it at atomistic level. As shown, the quotient-encoding-propagating-decoding scheme generates within a few hours protein folding pathways with experimentally verified outcomes. By contrast, MD computations covering comparable timespans would take over a hundred days on special-purpose supercomputers. Thus, quotient space constitutes a model for hierarchical understanding of MD simulation while enabling access to realistic timescales.