Feng's paper published in ACS Applied Materials & Interfaces - Optimizing WS2 Growth with Bayesian Optimization
Feng's paper published in ACS Applied Materials & Interfaces - Optimizing WS2 Growth with Bayesian Optimization
October 15, 2024
#Publication
#Machine Learning
#Bayesian Optimization
#Chemical Vapor Deposition
#WS2
#2D Material
Accelerating 2D Material Synthesis with Machine Learning
Published in ACS Applied Materials & Interfaces, 2024
Our latest research employs Bayesian optimization (BO), a machine learning technique, to optimize the growth of monolayer WS2 through chemical vapor deposition (CVD). By maximizing photoluminescence (PL) intensity, we achieved an impressive 86.6% increase in only 13 rounds of optimization. This work demonstrates the potential of BO to surpass traditional methods like random search, offering faster convergence to optimal growth conditions.
Read more on the impact of ML-driven approaches in advancing 2D materials for next-generation technologies.