Controlled growth of high-quality SnSe nanoplates assisted by machine learning

Abstract

Machine learning (ML) approaches have emerged as powerful tools to accelerate materials discovery and optimization, offering a sustainable alternative to traditional trial-and-error methods in exploratory experiments. This study demonstrates the application of ML for controlled chemical vapor deposition (CVD) growth of SnSe nanoplates (NPs), a promising thermoelectric material. Four ML regression models are implemented to predict the side length (SL) of SnSe NPs based on CVD growth parameters. The GPR model exhibits the best performance in predicting the SL of SnSe NPs, with a coefficient of determination of 0.996, a root-mean-square error of 0.516 µm, and a mean absolute error of 0.296 µm on the test set. Then, the predicted SL of SnSe NPs is optimized through the Bayesian optimization algorithm, and the maximum SL of SnSe NPs is identified to be 32.12 µm. Validation experiments confirm the reliability of the predicted results from the constructed GPR model, with relative errors below 8% between the predicted and experimental results. These results demonstrate the robustness of ML in predicting and optimizing the CVD growth of SnSe NPs, highlighting its potential to accelerate material development and contribute to the sustainable advancement of thermoelectric materials by significantly reducing time, costs, and resource consumption associated with traditional experimental methods.