Strategic Integration of Machine Learning in the Design of Excellent Hybrid Perovskite Solar Cells

The photoelectric conversion efficiency (PCE) of perovskites remains beneath the Shockley-Queisser limit, despite its significant potential for solar cell applications. The present focus is on investigating potential multicomponent perovskite candidates, particularly on the application of machine le...

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Bibliographic Details
Main Author:	Zhaosheng Zhang (4603021) (author)
Other Authors:	Sijia Liu (1751842) (author), Qing Xiong (136442) (author), Yanbo Liu (180774) (author)
Published:	2025
Subjects:	Cell Biology Cancer Science Policy Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified vgg16 surpassed xception solar cell applications selecting solar materials random forests lagged photoelectric conversion efficiency including decision trees graph neural networks exceptional photoelectric properties convolutional neural networks centered symmetry functions body tensor representation least favorable performance perovskites remains beneath ewald sum matrix 8 </ sub 3 </ sub 2 </ sup 2 </ sub decision tree models cnn models utilizing throughput screening method substitutional defects compared machine learning establishes exhibited superior performance efficiently identify high performance perovskites throughput screening machine learning r </ sine matrix >< sup gcsconv exhibited efficientnetv2b0 exhibited gnn models strategic integration slight advantage significant potential robust foundation results indicated queisser limit present focus ideal combination gnns ). gcsconv outperformed data set considerable improvement cnns ), band gaps adjacency matrices acsf ),
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Strategic Integration of Machine Learning in the Design of Excellent Hybrid Perovskite Solar Cells

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