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.