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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2025
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| _version_ | 1852023664627679232 |
|---|---|
| author | Zhaosheng Zhang (4603021) |
| author2 | Sijia Liu (1751842) Qing Xiong (136442) Yanbo Liu (180774) |
| author2_role | author author author |
| author_facet | Zhaosheng Zhang (4603021) Sijia Liu (1751842) Qing Xiong (136442) Yanbo Liu (180774) |
| author_role | author |
| dc.creator.none.fl_str_mv | Zhaosheng Zhang (4603021) Sijia Liu (1751842) Qing Xiong (136442) Yanbo Liu (180774) |
| dc.date.none.fl_str_mv | 2025-01-13T06:58:10Z |
| dc.identifier.none.fl_str_mv | 10.1021/acs.jpclett.4c03580.s002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Strategic_Integration_of_Machine_Learning_in_the_Design_of_Excellent_Hybrid_Perovskite_Solar_Cells/28193341 |
| dc.rights.none.fl_str_mv | CC BY-NC 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 ), |
| dc.title.none.fl_str_mv | Strategic Integration of Machine Learning in the Design of Excellent Hybrid Perovskite Solar Cells |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | 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 learning to expedite band gap screening. To efficiently identify high-performance perovskites, we utilized a data set of 1346 hybrid organic–inorganic perovskites and employed 11 machine learning models, including decision trees, convolutional neural networks (CNNs), and graph neural networks (GNNs). Four descriptors were utilized for high-throughput screening: sine matrix, Ewald sum matrix, atom-centered symmetry functions (ACSF), and many-body tensor representation (MBTR). The results indicated that LightGBM and CatBoost somewhat surpassed XGBoost in decision tree models, but random forests lagged. Among the CNN models utilizing the same four descriptors, CustomCNN and VGG16 surpassed Xception, while EfficientNetV2B0 exhibited the least favorable performance. When the sine matrix and Ewald sum matrix served as adjacency matrices in GNN models, GCSConv exhibited a considerable improvement over GATConv and a slight advantage over GCNConv. Significantly, GCSConv outperformed other models when utilized with the Ewald sum matrix. The ideal combination of descriptors and algorithms identified was MBTR + CustomCNN, with an <i>R</i><sup>2</sup> of 0.94. Subsequently, three perovskites exhibiting appropriate Heyd–Scuseria–Ernzerhof (HSE06) band gaps were identified to define the defects. Among them, CH<sub>3</sub>C(NH<sub>2</sub>)<sub>2</sub>SnI<sub>3</sub> exhibited superior performance in both vacancy and substitutional defects compared to C<sub>3</sub>H<sub>8</sub>NSnI<sub>3</sub> and (CH<sub>3</sub>)<sub>2</sub>NH<sub>2</sub>SnI<sub>3</sub>. This high-throughput screening method with machine learning establishes a robust foundation for selecting solar materials with exceptional photoelectric properties. |
| eu_rights_str_mv | openAccess |
| id | Manara_7ba34bd9bd85d2ee78a7b824098a18d4 |
| identifier_str_mv | 10.1021/acs.jpclett.4c03580.s002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28193341 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY-NC 4.0 |
| spelling | Strategic Integration of Machine Learning in the Design of Excellent Hybrid Perovskite Solar CellsZhaosheng Zhang (4603021)Sijia Liu (1751842)Qing Xiong (136442)Yanbo Liu (180774)Cell BiologyCancerScience PolicySpace ScienceBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedvgg16 surpassed xceptionsolar cell applicationsselecting solar materialsrandom forests laggedphotoelectric conversion efficiencyincluding decision treesgraph neural networksexceptional photoelectric propertiesconvolutional neural networkscentered symmetry functionsbody tensor representationleast favorable performanceperovskites remains beneathewald sum matrix8 </ sub3 </ sub2 </ sup2 </ subdecision tree modelscnn models utilizingthroughput screening methodsubstitutional defects comparedmachine learning establishesexhibited superior performanceefficiently identify highperformance perovskitesthroughput screeningmachine learningr </sine matrix>< supgcsconv exhibitedefficientnetv2b0 exhibitedgnn modelsstrategic integrationslight advantagesignificant potentialrobust foundationresults indicatedqueisser limitpresent focusideal combinationgnns ).gcsconv outperformeddata setconsiderable improvementcnns ),band gapsadjacency matricesacsf ),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 learning to expedite band gap screening. To efficiently identify high-performance perovskites, we utilized a data set of 1346 hybrid organic–inorganic perovskites and employed 11 machine learning models, including decision trees, convolutional neural networks (CNNs), and graph neural networks (GNNs). Four descriptors were utilized for high-throughput screening: sine matrix, Ewald sum matrix, atom-centered symmetry functions (ACSF), and many-body tensor representation (MBTR). The results indicated that LightGBM and CatBoost somewhat surpassed XGBoost in decision tree models, but random forests lagged. Among the CNN models utilizing the same four descriptors, CustomCNN and VGG16 surpassed Xception, while EfficientNetV2B0 exhibited the least favorable performance. When the sine matrix and Ewald sum matrix served as adjacency matrices in GNN models, GCSConv exhibited a considerable improvement over GATConv and a slight advantage over GCNConv. Significantly, GCSConv outperformed other models when utilized with the Ewald sum matrix. The ideal combination of descriptors and algorithms identified was MBTR + CustomCNN, with an <i>R</i><sup>2</sup> of 0.94. Subsequently, three perovskites exhibiting appropriate Heyd–Scuseria–Ernzerhof (HSE06) band gaps were identified to define the defects. Among them, CH<sub>3</sub>C(NH<sub>2</sub>)<sub>2</sub>SnI<sub>3</sub> exhibited superior performance in both vacancy and substitutional defects compared to C<sub>3</sub>H<sub>8</sub>NSnI<sub>3</sub> and (CH<sub>3</sub>)<sub>2</sub>NH<sub>2</sub>SnI<sub>3</sub>. This high-throughput screening method with machine learning establishes a robust foundation for selecting solar materials with exceptional photoelectric properties.2025-01-13T06:58:10ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1021/acs.jpclett.4c03580.s002https://figshare.com/articles/dataset/Strategic_Integration_of_Machine_Learning_in_the_Design_of_Excellent_Hybrid_Perovskite_Solar_Cells/28193341CC BY-NC 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/281933412025-01-13T06:58:10Z |
| spellingShingle | Strategic Integration of Machine Learning in the Design of Excellent Hybrid Perovskite Solar Cells Zhaosheng Zhang (4603021) 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 ), |
| status_str | publishedVersion |
| title | Strategic Integration of Machine Learning in the Design of Excellent Hybrid Perovskite Solar Cells |
| title_full | Strategic Integration of Machine Learning in the Design of Excellent Hybrid Perovskite Solar Cells |
| title_fullStr | Strategic Integration of Machine Learning in the Design of Excellent Hybrid Perovskite Solar Cells |
| title_full_unstemmed | Strategic Integration of Machine Learning in the Design of Excellent Hybrid Perovskite Solar Cells |
| title_short | Strategic Integration of Machine Learning in the Design of Excellent Hybrid Perovskite Solar Cells |
| title_sort | Strategic Integration of Machine Learning in the Design of Excellent Hybrid Perovskite Solar Cells |
| topic | 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 ), |