Application of machine learning in perovskite materials and devices:A review

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外文摘要：Metal-halide hybrid perovskite materials are excellent candidates for solar cells and photoelectric devices.In recent years,machine learning(ML)techniques have developed rapidly in many fields and provided ideas for material discovery and design.ML can be applied to discover new materials quickly and effectively,with significant savings in resources and time compared with traditional experiments and density functional theory(DFT)calculations.In this review,we present the application of ML in per-ovskites and briefly review the recent works in the field of ML-assisted perovskite design.Firstly,the advantages of perovskites in solar cells and the merits of ML applied to perovskites are discussed.Secondly,the workflow of ML in perovskite design and some basic ML algorithms are introduced.Thirdly,the applications of ML in predicting various properties of perovskite materials and devices are reviewed.Finally,we propose some prospects for the future development of this field.The rapid devel-opment of ML technology will largely promote the process of materials science,and ML will become an increasingly popular method for predicting the target properties of materials and devices.

外文关键词：

Machine learningPerovskiteMaterials designBandgap engineeringStabilityCrystal structure

作者：

Ming Chen、Zhenhua Yin、Zhicheng Shan、Xiaokai Zheng、Lei Liu、Zhonghua Dai、Jun Zhang、Shengzhong(Frank) Liu、Zhuo Xu

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作者单位：

School of Electric Power,Civil Engineering and Architecture,College of Physics and Electronics Engineering,State Key Laboratory of Quantum Optics and Quantum Optics Devices,Shanxi University,Taiyuan 030006,Shanxi,China

Key Laboratory of Applied Surface and Colloid Chemistry,National Ministry of Education,Shaanxi Engineering Lab for Advanced Energy Technology,School of Materials Science and Engineering,Shaanxi Normal University,Xi'an 710119,Shaanxi,China

State Key Laboratory of Catalysis,Dalian National Laboratory for Clean Energy,Dalian Institute of Chemical Physics,Chinese Academy of Sciences,Dalian 116023,Liaoning,China

基金：

Strategic Priority Research Program of the Chinese Academy of SciencesNational Natural Science Foundation of ChinaOpen Competition Mechanism to Select The Best Candidates Project in Jinzhong Science and Technology BureauDNL Cooperation Fund CAS111 Project

项目编号：

XDA1704050662005148/12004235J202101DNL180311B14041

出版年：

2024

DOI：

10.1016/j.jechem.2024.02.035

能源化学

中国科学院大连化学物理研究所中国科学院成都有机化学研究所

能源化学

CSTPCDEI

影响因子：0.654

ISSN：2095-4956

年,卷(期)：2024.94(7)