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通过不平衡学习预测杂化有机-无机钙钛矿的实验可成形性。

Predicting Experimental Formability of Hybrid Organic-Inorganic Perovskites via Imbalanced Learning.

机构信息

Materials Genome Institute, Shanghai University, Shanghai 200444, China.

Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China.

出版信息

J Phys Chem Lett. 2022 Apr 7;13(13):3032-3038. doi: 10.1021/acs.jpclett.2c00603. Epub 2022 Mar 29.

DOI:10.1021/acs.jpclett.2c00603
PMID:35348327
Abstract

Hybrid organic-inorganic perovskites (HOIPs) have gained lots of attention in the photovoltaic field, but their further development is restrained by contaminant and stability. More potential HOIPs should be explored for photovoltaic devices. In this work, we collected 539 HOIPs and 24 non-HOIPs experimentally synthesized to explore novel compositions of HOIPs. An imbalanced learning was carried out, and the best classification model achieved a leaving-one-out cross-validation accuracy of 100.0% and a test accuracy of 96.1%. The A site atomic radii (), A site ionic radius (), and tolerance factor () were identified as the most important features. < 2.72 Å, < 2.65 Å, and < 1.01 contributed to perovskite formability, and the formability possibilities of the corresponding samples were over 90.0%. Potential A site organic fragments were identified for perovskite solar cells, such as dimethylamine, hydroxylamine, hydrazine, . Finally, three new Sn-Ge mixed systems of HOIPs were successfully synthesized, which was consistent with the model predictions.

摘要

杂化有机-无机钙钛矿 (HOIPs) 在光伏领域引起了广泛关注,但它们的进一步发展受到污染物和稳定性的限制。应该探索更多潜在的 HOIPs 用于光伏器件。在这项工作中,我们收集了 539 种实验合成的 HOIPs 和 24 种非 HOIPs,以探索 HOIPs 的新型组成。进行了不平衡学习,最佳分类模型在留一法交叉验证中的准确率达到了 100.0%,测试准确率达到了 96.1%。A 位原子半径 ()、A 位离子半径 () 和容忍因子 () 被确定为最重要的特征。 < 2.72 Å、 < 2.65 Å 和 < 1.01 有利于钙钛矿的形成,相应样品的形成可能性超过 90.0%。为钙钛矿太阳能电池确定了潜在的 A 位有机片段,例如二甲胺、羟胺、肼、 。最后,成功合成了三种新的 Sn-Ge 混合体系的 HOIPs,这与模型预测一致。

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