Bayesian Optimization for Controlled Chemical Vapor Deposition Growth of WS2

We applied Bayesian optimization (BO), a machine learning (ML) technique, to optimize the growth conditions of monolayer WS2 using photoluminescence (PL) intensity as the objective function. Through iterative experiments guided by BO, an improvement of 86.6% in PL intensity is achieved within 13 optimization rounds. Statistical analysis revealed the relationships between growth conditions and PL intensity, highlighting the importance of critical conditions, including the tungsten source concentration and Ar flow rate. Furthermore, the effectiveness of BO is demonstrated by comparison with random search, showing its ability to converge to optimal conditions with fewer iterations. This research highlights the potential of ML-driven approaches in accelerating material synthesis and optimization processes, paving the way for advances in two-dimensional (2D) material-based technologies.