Challenges, Opportunities, and Prospects in Metal Halide Perovskites from Theoretical and Machine Learning Perspectives. Issue 45 (6th October 2022)

• Record Type: Journal Article
• Authors: Myung, Chang Woo; Hajibabaei, Amir; Cha, Ji‐Hyun; Ha, Miran; Kim, Junu; Kim, Kwang S.
• Is Part Of: Advanced energy materials. Volume 12:Issue 45(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-06
• Subjects: excited states dynamics -- kinetic processes -- machine learning potential -- machine learning -- materials design -- metal halide perovskites -- nanodomain; Energy harvesting -- Materials -- Periodicals; Energy conversion -- Materials -- Periodicals; Energy storage -- Materials -- Periodicals; Photovoltaics -- Periodicals; Fuel cells -- Periodicals; Thermoelectric materials -- Periodicals; 621.31
• DOI: 10.1002/aenm.202202279
• Languages: English
• ISSNs: 1614-6832

Abstract

Abstract: Metal halide perovskite (MHP) is a promising next generation energy material for various applications, such as solar cells, light emitting diodes, lasers, sensors, and transistors. MHPs show excellent mechanical, dielectric, photovoltaic, photoluminescence, and electronic properties, and such intriguing physical and chemical properties have drawn attention recently. However, there exists a chasm between the successful applications of MHPs and theoretical understandings. The difficulty arises from the intrinsic properties of MHPs, including structural disorder, ionic interactions, nonadiabatic effects, and composition diversity. Machine learning (ML) approaches have shown great promise as a tool to overcome the theoretical obstacles in many fields of science. In this perspective, the pending theoretical challenges from experiments are overviewed and promising ML approaches, including ab initio ML potentials, materials design/optimization models, and data mining strategies are proposed. Possible roles and pipelines of ML frameworks are highlighted to close the gap between experiment and theory in MHPs. Abstract : Metal halide perovskite (MHP) is a promising next generation energy material for various applications and shows excellent physical and chemical properties. This perspective article overviews and discusses useful machine learning approaches for a deeper theoretical understanding of MHPs and successful design and optimization of MHPs.