Data-Driven Perovskite Design via High-Throughput Simulation and Machine Learning

作者：Yidi Wang, Dan Sun, Bei Zhao, Tianyu Zhu, Chengcheng Liu, Zixuan Xu, Tianhang Zhou, Chunming Xu · 发表于：Processes · 年份：2025 · DOI：10.3390/pr13103049 · 被引用次数：8 · 研究领域：Perovskite Materials and Applications、Machine Learning in Materials Science、Electronic and Structural Properties of Oxides

Perovskites (ABX3) exhibit remarkable potential in optoelectronic conversion, catalysis, and diverse energy-related fields. However, the tunability of A, B, and X-site compositions renders conventional screening methods labor-intensive and inefficient. This review systematically synthesizes the roles of physical simulations and machine learning (ML) in accelerating perovskite discovery. By harnessing existing experimental datasets and high-throughput computational results, ML models elucidate structure-property relationships and predict performance metrics for solar cells, (photo)electrocatalysts, oxygen carriers, and energy-storage materials, with experimental validation confirming their predictive reliability. While data scarcity and heterogeneity inherently limit ML-based prediction of material property, integrating high-throughput computational methods as external mechanistic constraints—supplementing standardized, large-scale training data and imposing loss penalties—can improve accuracy and efficiency in bandgap prediction and defect engineering. Moreover, although embedding high-throughput simulations into ML architectures remains nascent, physics-embedded approaches (e.g., symmetry-aware networks) show increasing promise for enhancing physical consistency. This dual-driven paradigm, integrating data and physics, provides a versatile framework for perovskite design, achieving both high predictive accuracy and interpretability—key milestones toward a rational design str...