Graph Neural Network-Assisted Machine Learning for High-Throughput Buried Interfacial Materials Screening in Antisolvent-Free Perovskite Solar Cells

Gai Liu; K Liu; Haoyu Cai; Biqi He; Yongjun Liu; Yao Long; Jiayi Xu; Hongfei Zhang; Jiawei Luo; Lingmin Liu; Tong Wu; Ze-Qian Guo; Juan Zhao; Yi-Bing Cheng; Jie Zhong

doi:10.1021/acsami.6c06418

July 1, 2026ACS Applied Materials & Interfaces

Graph Neural Network-Assisted Machine Learning for High-Throughput Buried Interfacial Materials Screening in Antisolvent-Free Perovskite Solar Cells

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Key Points

This research aims to enhance the exploration of new materials for perovskite solar cells using advanced machine learning techniques.
Employs graph neural networks to assist in material screening.
Utilizes high-throughput techniques for efficient material evaluation.
Focuses on antisolvent-free processing methods in cell fabrication.
Achieved a voltage of 1.184 V in tested materials.
Reported a fill factor of 86.05% for the optimized solar cells.

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Implication

Demonstrates efficient materials screening in perovskite solar cells, suggesting scalability.

Abstract

) of 1.184 V and a fill factor (FF) of 86.05% using antisolvent-free processing. This work exemplifies an efficient route for scalable and rapid exploration of new materials for PSCs.

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Liu et al. (2026) studied this question.

synapsesocial.com/papers/6a4754f05c29257aa257a34e https://doi.org/10.1021/acsami.6c06418

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Graph Neural Network-Assisted Machine Learning for High-Throughput Buried Interfacial Materials Screening in Antisolvent-Free Perovskite Solar Cells

Key Points

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Implication

Abstract

Discussion

Cite This Study

Graph Neural Network-Assisted Machine Learning for High-Throughput Buried Interfacial Materials Screening in Antisolvent-Free Perovskite Solar Cells

Key Points

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Implication

Abstract

Discussion

Cite This Study

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