{"title":"机器学习辅助设计用于高性能过氧化物太阳能电池的空穴传输材料","authors":"Muhammad Saqib , Uzma Shoukat , Mohamed Mohamed Soliman , Shahida Bashir , Mudassir Hussain Tahir , Hamdy Khamees Thabet , Mohamed Kallel","doi":"10.1016/j.chemphys.2024.112515","DOIUrl":null,"url":null,"abstract":"<div><div>In recent years, the advancement of perovskite solar cells has accelerated, leading to continuous performance improvements. Over the past few years, machine learning (ML) has gained popularity among scientists researching perovskite solar cells. In this study, ML is used to screen hole-transporting materials for perovskite solar cells. To construct machine-learning (ML) models, data from prior investigations are collected. Out of four machine learning algorithms trained for predicting reorganization energy (Rh), the gradient boosting regression model stood out as the most effective, attaining an R<sup>2</sup> value of 0.89. Data visualization analysis is then utilized to scrutinize the patterns within the dataset. 10,000 new compounds are generated. Chemical space of generated compounds is visualized using various measures. Minor structural modifications resulted in only a slight alteration in reorganization energy (Rh). The newly introduced multidimensional framework has the potential to efficiently screen materials in a short amount of time.</div></div>","PeriodicalId":272,"journal":{"name":"Chemical Physics","volume":"589 ","pages":"Article 112515"},"PeriodicalIF":2.0000,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning assisted designing of hole-transporting materials for high performance perovskite solar cells\",\"authors\":\"Muhammad Saqib , Uzma Shoukat , Mohamed Mohamed Soliman , Shahida Bashir , Mudassir Hussain Tahir , Hamdy Khamees Thabet , Mohamed Kallel\",\"doi\":\"10.1016/j.chemphys.2024.112515\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In recent years, the advancement of perovskite solar cells has accelerated, leading to continuous performance improvements. Over the past few years, machine learning (ML) has gained popularity among scientists researching perovskite solar cells. In this study, ML is used to screen hole-transporting materials for perovskite solar cells. To construct machine-learning (ML) models, data from prior investigations are collected. Out of four machine learning algorithms trained for predicting reorganization energy (Rh), the gradient boosting regression model stood out as the most effective, attaining an R<sup>2</sup> value of 0.89. Data visualization analysis is then utilized to scrutinize the patterns within the dataset. 10,000 new compounds are generated. Chemical space of generated compounds is visualized using various measures. Minor structural modifications resulted in only a slight alteration in reorganization energy (Rh). The newly introduced multidimensional framework has the potential to efficiently screen materials in a short amount of time.</div></div>\",\"PeriodicalId\":272,\"journal\":{\"name\":\"Chemical Physics\",\"volume\":\"589 \",\"pages\":\"Article 112515\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chemical Physics\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0301010424003446\",\"RegionNum\":3,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemical Physics","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0301010424003446","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Machine learning assisted designing of hole-transporting materials for high performance perovskite solar cells
In recent years, the advancement of perovskite solar cells has accelerated, leading to continuous performance improvements. Over the past few years, machine learning (ML) has gained popularity among scientists researching perovskite solar cells. In this study, ML is used to screen hole-transporting materials for perovskite solar cells. To construct machine-learning (ML) models, data from prior investigations are collected. Out of four machine learning algorithms trained for predicting reorganization energy (Rh), the gradient boosting regression model stood out as the most effective, attaining an R2 value of 0.89. Data visualization analysis is then utilized to scrutinize the patterns within the dataset. 10,000 new compounds are generated. Chemical space of generated compounds is visualized using various measures. Minor structural modifications resulted in only a slight alteration in reorganization energy (Rh). The newly introduced multidimensional framework has the potential to efficiently screen materials in a short amount of time.
期刊介绍:
Chemical Physics publishes experimental and theoretical papers on all aspects of chemical physics. In this journal, experiments are related to theory, and in turn theoretical papers are related to present or future experiments. Subjects covered include: spectroscopy and molecular structure, interacting systems, relaxation phenomena, biological systems, materials, fundamental problems in molecular reactivity, molecular quantum theory and statistical mechanics. Computational chemistry studies of routine character are not appropriate for this journal.