Graph Neural Network-Assisted Machine Learning for High-Throughput Buried Interfacial Materials Screening in Antisolvent-Free Perovskite Solar Cells
Rattachement africain : cn, jp. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Numerous interfacial materials have been explored for efficient perovskite solar cells (PSCs). The identification of optimal candidates from the vast chemical space remains a difficult and costly task. Artificial intelligence (AI)-assisted materials screening has emerged as a convenient route to benefit and promote the pace of PSC research. Here, we propose a two-step cascade screening strategy that integrates a contrastive learning-based graph neural network (CLGNN) with a gradient boosting decision tree regressor (GBDTR) to balance high-throughput and predictive accuracy. Among them, CLGNN is a graph contrastive learning model trained on large-scale unlabeled molecular graph topologies, whereas GBDTR is a supervised regression model trained on a manually curated and labeled database. Using CLGNN, we screened one million molecules on a graphics processing unit (GPU) and shortlisted 128 candidates within 10 min. This step substantially reduced the number of molecules requiring subsequent density functional theory (DFT) calculations and experimental validation. We then developed and evaluated multiple regression models based on multidimensional physicochemical descriptors to accurately predict device performance. Two effective buried-interface modifiers, 4,4′-iminodibenzoic acid (4,4′-IDA) and 4,4′-biphenyldicarboxylic acid (4,4′-BA), were selected, which delivered a high-power conversion efficiency of 26.10% with an open-circuit voltage ( V OC ) 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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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Graph Neural Network-Assisted Machine Learning for High-Throughput Buried Interfacial Materials Screening in Antisolvent-Free Perovskite Solar Cells
- Date Crossref
- 01/07/2026
- Éditeur
- American Chemical Society (ACS)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Wuhan University of Technology pays non établi dans la noticeUniversité ou école supérieure
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Chery Automobile (China) pays non établi dans la noticeEntreprise
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Japan Automobile Research Institute pays non établi dans la noticeInstitution
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State Key Laboratory of Advanced Technology For Materials Synthesis and Processing pays non établi dans la noticeStructure de recherche
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Ji Hua Laboratory pays non établi dans la noticeStructure de recherche
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School of Automobile Engineering pays non établi dans la noticeUniversité ou école supérieure
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Foshan Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory pays non établi dans la noticeStructure de recherche
Wuhan University of Technology, Chery Automobile (China) et Japan Automobile Research Institute, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.