Ionic liquid-assisted preparation of Ag–Zn–In–S quaternary quantum dot thin films and luminescence performance optimized by machine learning

Abstract

Machine learning-assisted synthesis of new materials has been an ongoing hot topic. Luminescent Ag–Zn–In–S quaternary semiconductor quantum dot thin films have great application potential in many fields because of their excellent photoelectric performance. In this study, an environmentally friendly ionic liquid-assisted approach was employed to prepare high-performance Ag–Zn–In–S luminescent thin films, and machine learning methods were applied to screen the optimal preparation conditions. The photoluminescence (PL) performance, crystal structure, and morphology of the thin films were also been studied. The initial data set was obtained by high-throughput experiments. By comparing machine learning algorithms of Xgboost, Random Forest, Bagging, SVM, Extra Tree, and Gradient Boosting, the Random Forest achieved the best accuracy (r = 0.84, R2 = 0.847) and was selected to predict the optimal preparation conditions of Ag–Zn–In–S quaternary quantum dot thin films. The machine learning predicted results are in great agreement with the subsequent experimental results, which verifies the accuracy and effectiveness of the machine learning model. This study offers a theoretical foundation for preparing optimal inorganic quantum dot luminescent materials using machine learning.