REVIEW article
Front. Mater.
Sec. Computational Materials Science
Data-Driven AI Approaches for Screening High-Efficiency, Stable, and Lead-Free Perovskite Photovoltaic Materials: A Review
Provisionally accepted
- 1Xijing University, Xi'an, China
- 2Beijing Chaoyang Foreign Language School, Beijing, China
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With the global increase in energy demand and environmental awareness, it has become crucial to develop new types of energy materials that are efficient, stable and environmentally friendly. Lead-free perovskite materials have garnered attention due to their unique crystal structure (ABX3) and photoelectric properties, particularly demonstrating great potential for applications such as photovoltaics, photodetectors, catalysis, and display lighting. However, the lead toxicity of traditional lead-containing perovskite materials limits their large-scale commercialization. Therefore, the research on stable and non-toxic lead-free perovskite materials has become a current hot topic in scientific research. In recent years, artificial intelligence technology has brought about a transformation in the study of perovskite materials. This review focuses on the application of AI in lead-free perovskite research, including data collection, preprocessing, feature extraction, model training and prediction, reverse design and experimental verification. This paper aims to leverage AI technologies to drive data-informed and inverse-designed discovery processes, thereby improving the efficiency and success rate of lead-free perovskite materials screening, development, and performance optimization.
Keywords: artificial intelligence, Lead-free perovskite materials, Data-driven design, performance prediction, Interdisciplinary Collaboration
Received: 25 Jul 2025; Accepted: 21 Nov 2025.
Copyright: © 2025 Wang, Wang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Juan Wang, wjbiophysics@yeah.net
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