Advancing perovskite photovoltaic technology through machine learning‐driven automation DOI Creative Commons
Jiyun Zhang, Jianchang Wu, Vincent M. Le Corre

и другие.

InfoMat, Год журнала: 2025, Номер unknown

Опубликована: Фев. 24, 2025

Abstract Since its emergence in 2009, perovskite photovoltaic technology has achieved remarkable progress, with efficiencies soaring from 3.8% to over 26%. Despite these advancements, challenges such as long‐term material and device stability remain. Addressing requires reproducible, user‐independent laboratory processes intelligent experimental preselection. Traditional trial‐and‐error methods manual analysis are inefficient urgently need advanced strategies. Automated acceleration platforms have transformed this field by improving efficiency, minimizing errors, ensuring consistency. This review summarizes recent developments machine learning‐driven automation for photovoltaics, a focus on application new transport discovery, composition screening, preparation optimization. Furthermore, the introduces concept of self‐driven Autonomous Material Device Acceleration Platforms (AMADAP) discusses potential it may face. approach streamlines entire process, discovery performance improvement, ultimately accelerating development emerging technologies. image

Язык: Английский

Advancing perovskite photovoltaic technology through machine learning‐driven automation DOI Creative Commons
Jiyun Zhang, Jianchang Wu, Vincent M. Le Corre

и другие.

InfoMat, Год журнала: 2025, Номер unknown

Опубликована: Фев. 24, 2025

Abstract Since its emergence in 2009, perovskite photovoltaic technology has achieved remarkable progress, with efficiencies soaring from 3.8% to over 26%. Despite these advancements, challenges such as long‐term material and device stability remain. Addressing requires reproducible, user‐independent laboratory processes intelligent experimental preselection. Traditional trial‐and‐error methods manual analysis are inefficient urgently need advanced strategies. Automated acceleration platforms have transformed this field by improving efficiency, minimizing errors, ensuring consistency. This review summarizes recent developments machine learning‐driven automation for photovoltaics, a focus on application new transport discovery, composition screening, preparation optimization. Furthermore, the introduces concept of self‐driven Autonomous Material Device Acceleration Platforms (AMADAP) discusses potential it may face. approach streamlines entire process, discovery performance improvement, ultimately accelerating development emerging technologies. image

Язык: Английский

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