P. Rajak,A. Krishnamoorthy et al., Autonomous Reinforcement Learning Agent for CVD Synthesis of Quantum Materials
Quantum Material Synthesis by
Reinforcement Learning ,
NeurIPS workshop, Machine Learning and the Physical Sciences.
Designing experimental synthesis condition of materials is a challenging task as it involves decision making process of time-dependent synthesis parameters. Further, these experiments take very long time to perform, and thus its not feasible to use the active learning approaches to directly learn the optimal synthesis condition. Here, we have used Offline Model based Reinforcement Leaning (RL) to propose the optimal synthesis condition of 2D quantum material. After trainng, RL agent not only outperformed the random baseline model by more then 50% by desiging optimal policy of synthesis condtiom of reaction temperature and reactant gas concentration but also provided mechanistic insight of synthesis process itself in terms of synthesis parameters.