Multi-Task Reinforcement Learning for Autonomous Material Design

Finding strategies to design novel functional materials and structures is one of the central endeavors of chemical and materials sciences. However, optimal material designs involve high dimensional parameter space search and time dependent sequential decision-making process, which results in development of new ML techniques that are capable of implicit decision-making over long period of time with little human supervision. In this talk, I will discuss our recent work on multi-task reinforcement learning (RL) for automated material-discovery with target properties and predictive synthesis of quantum materials. Further, I will discuss the mechanistic insight provided by RL workflow related to material design and synthesis with respect to other ML techniques such as active learning and generative models.