Feng's paper published in ACS Applied Materials & Interfaces - Optimizing WS2 Growth with Bayesian Optimization

Tue, 15 Oct 2024 00:00:00 +0000

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.

Chemical Vapor Deposition |

Feng's paper published in ACS Applied Materials & Interfaces - Optimizing WS2 Growth with Bayesian Optimization

Accelerating 2D Material Synthesis with Machine Learning