  Diffusion in solids is a slow process that dictates rate-limiting processes
in key chemical reactions. Unlike crystalline solids that offer well-defined
diffusion pathways, the lack of similar structural motifs in amorphous or
glassy materials poses a great scientific challenge in estimating slow
diffusion time. To tackle this problem, we have developed an AI-guided
long-time atomistic simulation approach: Molecular Autonomous Pathfinder (MAP)
framework based on Deep Reinforcement Learning (RL), where RL agent is trained
to uncover energy efficient diffusion pathways. We employ Deep Q-Network
architecture with distributed prioritized replay buffer enabling fully online
agent training with accelerated experience sampling by an ensemble of
asynchronous agents. After training, the agents provide atomistic
configurations of diffusion pathways with their energy profile. We use a
piecewise Nudged Elastic Band to refine the energy profile of the obtained
pathway and corresponding diffusion time on the basis of transition state
theory. With MAP, we have successfully identified atomistic mechanisms along
molecular diffusion pathways in amorphous silica, with time scales comparable
to experiments.
