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DC Field | Value | Language |
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dc.citation.endPage | 121 | - |
dc.citation.startPage | 109 | - |
dc.citation.title | INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY | - |
dc.citation.volume | 10 | - |
dc.contributor.author | Bak, Taeju | - |
dc.contributor.author | Kim, Kyusun | - |
dc.contributor.author | Seo, Eunhyeok | - |
dc.contributor.author | Han, Jiye | - |
dc.contributor.author | Sung, Hyokyung | - |
dc.contributor.author | Jeon, Il | - |
dc.contributor.author | Jung, Im Doo | - |
dc.date.accessioned | 2023-12-21T13:07:56Z | - |
dc.date.available | 2023-12-21T13:07:56Z | - |
dc.date.created | 2023-09-06 | - |
dc.date.issued | 2023-01 | - |
dc.description.abstract | Tin (Sn) perovskite solar cells (PSCs) are the most promising alternatives to lead (Pb) PSCs, which pose a theoretical limitation on efficiency and an environmental threat. However, Sn PSCs are still in the early stage of development in comparison with the conventional Pb PSCs, and still require a considerable amount of time and effort to obtain an optimum structure via manual trial-and-error methods. Herein, we propose a machine learning (ML) approach to accelerate the design of the optimized structure of Sn PSCs with high efficiency. The proposed method uses K-fold cross-validation-based deep neural networks, thus maximizing the prediction and recommendation accuracy with a limited amount of experimental data recorded for the Sn PSCs. Our approach establishes a new appropriate Sn-PSC design based on an ML recommendation algorithm. The validation experiment reveals a three times higher efficiency of the ML-designed Sn PSCs (5.57%) than that of those designed through unguided fabrication trials (avg. 1.72%). | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY, v.10, pp.109 - 121 | - |
dc.identifier.doi | 10.1007/s40684-022-00417-z | - |
dc.identifier.issn | 2288-6206 | - |
dc.identifier.scopusid | 2-s2.0-85126089573 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/65337 | - |
dc.identifier.url | https://link.springer.com/article/10.1007/s40684-022-00417-z | - |
dc.identifier.wosid | 000767697000001 | - |
dc.language | 영어 | - |
dc.publisher | 한국정밀공학회 | - |
dc.title | Accelerated Design of High-Efficiency Lead-Free Tin Perovskite Solar Cells via Machine Learning | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Green & Sustainable Science & Technology;Engineering, Manufacturing;Engineering, Mechanical | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics;Engineering | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Deep neural network | - |
dc.subject.keywordAuthor | Recommendation algorithm | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Perovskite solar cells | - |
dc.subject.keywordAuthor | Lead-free perovskites | - |
dc.subject.keywordAuthor | Tin perovskites | - |
dc.subject.keywordPlus | HALIDE PEROVSKITES | - |
dc.subject.keywordPlus | IODIDE | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.subject.keywordPlus | STABILITY | - |
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