Hydrogen Diffusion through Polymer using Deep Reinforcement Learning

Robust and cost-effective hydrogen storage is considered as an enabling technology for carbon-free and renewable energy society. Hydrogen tank using polymer liner has been in market and already used in fuel cell electric vehicles and airplanes. Understanding of the fundamental mechanisms of hydrogen diffusion in polymer could greatly speed up the deployment of hydrogen energy infrastructure at scale. A computational framework that provides atomistic diffusion pathways at experimentally relevant time scale is ideal for this purpose, however, it is yet to be demonstrated. We have developed a novel deep reinforcement learning framework combined with transition state theory to efficiently identify molecular diffusion pathways in polymeric materials. Employing distributed replay buffer, an ensemble of agents quickly learns the complex energy landscape of the system of interest. Subsequently, the diffusion time of each pathway is estimated using transition state theory. With the distributed training framework we have achieved significant improvement in learning in terms of both the training metrics as well as the molecular diffusion time.