{"title":"有关过氧化物太阳能电池装置的数据驱动分析","authors":"","doi":"10.1016/j.cap.2024.09.003","DOIUrl":null,"url":null,"abstract":"<div><p>Recent advancements in artificial intelligence (AI) techniques have significantly influenced daily life and the forefront of research and development. Data-driven research using AI accelerates the resolution of complex problems and aids in uncovering previously unknown knowledge and scientific discoveries. In this study, we propose a data-driven approach for investigating perovskite solar cells, a vibrant area within renewable energy applications. This approach incorporates the generation of a robust dataset, developing an interpretable machine learning model based on knowledge-based feature selection, and analyzing the impacts of material properties on the device performance. Through this framework, we successfully constructed accurate predictive models for the efficiency of perovskite solar cells and assessed the importance of each feature. Our analysis demonstrates that our models effectively capture existing knowledge about perovskite solar cells and can potentially inform the design of new perovskite solar cell configurations.</p></div>","PeriodicalId":11037,"journal":{"name":"Current Applied Physics","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data-driven analysis on perovskite solar cell devices\",\"authors\":\"\",\"doi\":\"10.1016/j.cap.2024.09.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Recent advancements in artificial intelligence (AI) techniques have significantly influenced daily life and the forefront of research and development. Data-driven research using AI accelerates the resolution of complex problems and aids in uncovering previously unknown knowledge and scientific discoveries. In this study, we propose a data-driven approach for investigating perovskite solar cells, a vibrant area within renewable energy applications. This approach incorporates the generation of a robust dataset, developing an interpretable machine learning model based on knowledge-based feature selection, and analyzing the impacts of material properties on the device performance. Through this framework, we successfully constructed accurate predictive models for the efficiency of perovskite solar cells and assessed the importance of each feature. Our analysis demonstrates that our models effectively capture existing knowledge about perovskite solar cells and can potentially inform the design of new perovskite solar cell configurations.</p></div>\",\"PeriodicalId\":11037,\"journal\":{\"name\":\"Current Applied Physics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Applied Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1567173924002037\",\"RegionNum\":4,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Applied Physics","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1567173924002037","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Data-driven analysis on perovskite solar cell devices
Recent advancements in artificial intelligence (AI) techniques have significantly influenced daily life and the forefront of research and development. Data-driven research using AI accelerates the resolution of complex problems and aids in uncovering previously unknown knowledge and scientific discoveries. In this study, we propose a data-driven approach for investigating perovskite solar cells, a vibrant area within renewable energy applications. This approach incorporates the generation of a robust dataset, developing an interpretable machine learning model based on knowledge-based feature selection, and analyzing the impacts of material properties on the device performance. Through this framework, we successfully constructed accurate predictive models for the efficiency of perovskite solar cells and assessed the importance of each feature. Our analysis demonstrates that our models effectively capture existing knowledge about perovskite solar cells and can potentially inform the design of new perovskite solar cell configurations.
期刊介绍:
Current Applied Physics (Curr. Appl. Phys.) is a monthly published international journal covering all the fields of applied science investigating the physics of the advanced materials for future applications.
Other areas covered: Experimental and theoretical aspects of advanced materials and devices dealing with synthesis or structural chemistry, physical and electronic properties, photonics, engineering applications, and uniquely pertinent measurement or analytical techniques.
Current Applied Physics, published since 2001, covers physics, chemistry and materials science, including bio-materials, with their engineering aspects. It is a truly interdisciplinary journal opening a forum for scientists of all related fields, a unique point of the journal discriminating it from other worldwide and/or Pacific Rim applied physics journals.
Regular research papers, letters and review articles with contents meeting the scope of the journal will be considered for publication after peer review.
The Journal is owned by the Korean Physical Society.