Towards Exascale Bio-molecular Simulations with Artificial Intelligence Workflows

Molecular simulations take significant supercomputing resources, roughly between 40 and 60% of time. While emerging Exascale compute architectures promise to provide an unprecedented capability to simulate complex physical phenomena, they also pose a number of computational challenges for effective scaling such simulations. We describe our research in scaling bio-molecular simulations by tightly integrating artificial intelligence (AI) approaches using our Molecules library. We demonstrate Molecules can extract biophysically meaningful reaction coordinates from long time-scale simulations (post-processing and/or in situ) that can be interpreted with respect to experimental data. We then show that the AI-derived reaction coordinates can be used to accelerate molecular simulations, especially in discovering novel conformational states that can be used to drive additional exploration through high dimensional landscapes. Using reinforcement learning techniques we demonstrate that these states can sample novel folding pathways that have not been explored previously. Together, we show that our integrated workflow can effectively accelerate sampling of high dimensional conformational landscapes while simultaneously making use of emerging hardware features of Exascale architectures.