Utilization of Machine Learning Algorithms to Optimize the Photoluminescence Properties of Perovskite Thin Films Synthesized through Ionic Liquid-Based Techniques

With their excellent optical properties, perovskite thin
films
demonstrate vast potential for practical applications, ranging from
solid-state lighting to advanced display technique. In this work,
environmentally benign ionic liquids were employed to synthesize high-quality
CsPbBr3 perovskite films. Several carboxylate amine ionic
liquids were introduced to prepare CsPbBr3 perovskite thin
films. The optimal preparation conditions of the perovskite thin films
were screened and predicted by machine learning algorithms. The predicted
results are highly consistent with the subsequent verification experiments.
The employment of machine learning algorithms has been demonstrated
to result in enhanced PL intensity and improved film quality in perovskite
thin films. This work provided a new strategy for the utilization
of machine learning algorithms in perovskite luminescent materials.