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

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 role...

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Veröffentlicht in:Processes Jg. 13; H. 10; S. 3049
Hauptverfasser: Wang, Yidi, Sun, Dan, Zhao, Bei, Zhu, Tianyu, Liu, Chengcheng, Xu, Zixuan, Zhou, Tianhang, Xu, Chunming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.10.2025
Schlagworte:
Accuracy
Algorithms
Analysis
Catalysis
Computer applications
Critical phenomena
Crystal structure
Efficiency
Electrocatalysts
Embedding
Energy storage
Force and energy
Functional materials
Heterogeneity
Iodine
Learning algorithms
Learning strategies
Machine learning
Material properties
Optimization
Optoelectronics
Performance measurement
Performance prediction
Perovskite
Perovskites
Phase transitions
Photovoltaic cells
Point defects
Simulation
Simulation methods
Solar cells
ISSN:2227-9717, 2227-9717
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Zusammenfassung: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 strategy for functional materials discovery.
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ISSN:2227-9717
2227-9717
DOI:10.3390/pr13103049