Reinforcement Learning Agent for autonomous predictive material synthesis and transport pathways

Predictive material synthesis strategies for promising materials are time consuming using current experimental and computational techniques. Similarly, various transport related processes such as diffusion of small molecules through porous media is a challenging application to study due to combinatorically high pathways possible. We use offline and online Reinforcement Learning (RL) to find optimal time series sequence facilitating the synthesis of desired materials and transport through porous media. In this study, we will demonstrate various RL strategies such as Deep Q Networks (DQN), Policy Based Learning (REINFORCE) and Tree Search Based Methods (MCTS) to solve these challenging problems applicable to a wide range of disciplines. Offline RL is used for predicting optimal material synthesis route for a chemical vapor deposition and physical vapor deposition process. Further, online RL is used in conjunction with reactive molecular dynamics to study transport of molecules through porous media in real time.