Platform-conditioned machine learning and descriptor-group auditing for precursor additive discovery in perovskite solar cells

Jia Jia Peng; Mingtao Duan; Menglin Li; Yuyang He; Huiying Hao; Mingxia Li; Chen Zhang; Zhaoyang Chen; Qi Yu; Qingyi Yan; Jinhong Zhang; Jia Jiamin Jiamin Jia; Ruyi Tan; Hao Liu; Jie Xing; Jingjing Dong

doi:10.1039/d6ta04986a

January 1, 2026Journal of Materials Chemistry A

Platform-conditioned machine learning and descriptor-group auditing for precursor additive discovery in perovskite solar cells

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Key Points

Develop a platform-conditioned machine learning and auditing framework to identify and prioritize precursor additives for perovskite solar cells from heterogeneous published datasets.
Trained a platform-conditioned machine learning model with descriptor-group auditing on diverse literature data.
Screened and selected candidate precursor additives for experimental validation in perovskite solar cell fabrication.
Prioritized the additive 4-TBA through auditable, in-domain machine learning screening.
Demonstrated experimentally that 4-TBA enhances perovskite solar-cell power conversion efficiency and reduces current-voltage hysteresis.

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Implication

Machine learning analysis demonstrates improved efficiency and reduced hysteresis with 4-TBA in perovskite solar cells, highlighting a reliable additive discovery method.

Abstract

Platform-conditioned machine learning enables auditable, in-domain prioritization of precursor additives from heterogeneous literature data. The selected additive 4-TBA improves perovskite solar-cell efficiency and reduces hysteresis.

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Peng et al. (2026) studied this question.

synapsesocial.com/papers/6aa93a2dd6d58a1cc10c074e https://doi.org/10.1039/d6ta04986a

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Platform-conditioned machine learning and descriptor-group auditing for precursor additive discovery in perovskite solar cells

Key Points

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Abstract

Discussion

Cite This Study

Platform-conditioned machine learning and descriptor-group auditing for precursor additive discovery in perovskite solar cells

Key Points

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Implication

Abstract

Discussion

Cite This Study

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