Machine learning for perovskite materials design and discovery
TL
TL;DR
The latest applications of Machine learning in assisting perovskites discovery and suggestions on the future development prospects of ML in the field ofPerovskite materials are put forward.
Abstract
The development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of materials. In this review, we retrospected the latest applications of ML in assisting perovskites discovery. First, the development tendency of ML in perovskite materials publications in recent years was organized and analyzed. Second, the workflow of ML in perovskites discovery was introduced. Then the applications of ML in various properties of inorganic perovskites, hybrid organic–inorganic perovskites and double perovskites were briefly reviewed. In the end, we put forward suggestions on the future development prospects of ML in the field of perovskite materials.
References146 items
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Aqueous solvent-regulated crystallization and interfacial modification in perovskite solar cells with enhanced stability and performance
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Hydrogen halide-free synthesis of organohalides for organometal trihalide perovskite solar cells
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Machine learning (ML)-assisted optimization doping of KI in MAPbI3 solar cells
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An efficient and durable perovskite electrocatalyst for oxygen reduction in solid oxide fuel cells
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