From Formability to Bandgap: Machine Learning Accelerates the Discovery and Application of Perovskite Materials.

Journal: ACS nano
Published Date: Aug 6, 2025

Abstract

Perovskite materials are considered promising candidates for applications in solar cells, photodetectors, catalysts, and light-emitting diodes, owing to their exceptional physicochemical and structural properties. Recently, the integration of machine learning into perovskite research has revolutionized the discovery and optimization process by overcoming the limitations of traditional trial-and-error methods and computationally intensive first-principles calculations. This review examines the role of machine learning in predicting perovskite properties and advancing their practical applications. First, the representative literature and the development trend of machine learning in perovskite materials in recent years were organized and analyzed. Second, the workflow of machine learning for perovskite materials was delineated, accompanied by a brief introduction to the fundamental algorithms. Third, by analyzing the structure and composition of perovskite materials, the role of machine learning in accelerating the discovery of perovskites, particularly in predicting formability and bandgap, is detailed. Finally, four practical applications of machine learning on perovskite materials were presented, along with an innovative proposal of the potential challenges and future directions of machine learning in the field of perovskite materials. Overall, this review aims to provide comprehensive insights and practical guidance for perovskite research, fostering the further development of machine learning-accelerated discovery and application of perovskite materials.

Authors

  • Shiyan Wang
    Faculty of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian, 223003, Jiangsu, China. shiyanwanghyit@163.com.
  • Chaopeng Liu
    College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), State Key Laboratory of Flexible Electronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
  • Weiyao Hao
    College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), State Key Laboratory of Flexible Electronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
  • Yanling Zhuang
    College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), State Key Laboratory of Flexible Electronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
  • Xianjun Zhu
    College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), State Key Laboratory of Flexible Electronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
  • Longlu Wang
    College of Electronic and Optical Engineering, Institute of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications (NJUPT), Nanjing 210023, Jiangsu, P. R. China. b22020916@njupt.edu.cn.
  • Xianghong Niu
    College of Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
  • Shujuan Liu
    School of Information Science and Engineering, Shandong Normal University, Shandong, China.
  • Bing Chen
    Department of Critical Care Medicine, The Second Hospital of Tianjin Medical University, Tianjin, China.
  • Qiang Zhao
    Key Laboratory for Organic Electronics and Information Displays (KLOEID) & Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing University of Posts and Telecommunications, 9 Wenyuan Road, Nanjing 210023, China.

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