Fig. 3: Reinforcement Learning model for synthesis schedule design.
From: Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials
a Schematic of the RL-NADE model for optimizing schedules for MoS2 synthesis. b Comparison of structures generated by the RL-designed schedules against randomly generated schedules demonstrates that the RL-NADE model consistently identifies CVD synthesis schedules that generate highly crystalline products. c Validation of a promising RL-generated schedule using RMD simulations.
