{"title":"筛选高性能混合卤化物闪烁体:一个综合分析和预测模型","authors":"Maxim Molokeev, Nicolay Golovnev, Andrey Zolotov, Shuai Zhang, Zhiguo Xia","doi":"10.1021/acs.chemmater.4c03162","DOIUrl":null,"url":null,"abstract":"Machine learning models were applied to predict the scintillation performances of organic–inorganic hybrid metal halides (OIMHs), focusing on their photoluminescent quantum yield (PLQY). Random Forest and Decision Tree algorithms identified the most critical structural parameter of organic molecules influencing the M···M distance between metal ions and correlated PLQY value, with an optimal distance of approximately 8 Å correlating with enhanced luminescence efficiency. This prediction was experimentally validated through the synthesis of several OIMH compounds, demonstrating strong agreement between predicted and measured PLQY values. The machine learning approach not only enabled the screening of efficient compounds but also deepened the understanding of how structural factors, such as the structure of organic molecules, govern scintillation properties. These findings underscore the potential of machine learning in accelerating the development of next-generation luminescent materials with improved performance, offering a powerful tool for future material design and optimization.","PeriodicalId":33,"journal":{"name":"Chemistry of Materials","volume":"30 1","pages":""},"PeriodicalIF":7.0000,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Screening High-Performance Hybrid Halides Scintillators: A Comprehensive Analysis and Prediction Model\",\"authors\":\"Maxim Molokeev, Nicolay Golovnev, Andrey Zolotov, Shuai Zhang, Zhiguo Xia\",\"doi\":\"10.1021/acs.chemmater.4c03162\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Machine learning models were applied to predict the scintillation performances of organic–inorganic hybrid metal halides (OIMHs), focusing on their photoluminescent quantum yield (PLQY). Random Forest and Decision Tree algorithms identified the most critical structural parameter of organic molecules influencing the M···M distance between metal ions and correlated PLQY value, with an optimal distance of approximately 8 Å correlating with enhanced luminescence efficiency. This prediction was experimentally validated through the synthesis of several OIMH compounds, demonstrating strong agreement between predicted and measured PLQY values. The machine learning approach not only enabled the screening of efficient compounds but also deepened the understanding of how structural factors, such as the structure of organic molecules, govern scintillation properties. These findings underscore the potential of machine learning in accelerating the development of next-generation luminescent materials with improved performance, offering a powerful tool for future material design and optimization.\",\"PeriodicalId\":33,\"journal\":{\"name\":\"Chemistry of Materials\",\"volume\":\"30 1\",\"pages\":\"\"},\"PeriodicalIF\":7.0000,\"publicationDate\":\"2025-01-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chemistry of Materials\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1021/acs.chemmater.4c03162\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemistry of Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1021/acs.chemmater.4c03162","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Screening High-Performance Hybrid Halides Scintillators: A Comprehensive Analysis and Prediction Model
Machine learning models were applied to predict the scintillation performances of organic–inorganic hybrid metal halides (OIMHs), focusing on their photoluminescent quantum yield (PLQY). Random Forest and Decision Tree algorithms identified the most critical structural parameter of organic molecules influencing the M···M distance between metal ions and correlated PLQY value, with an optimal distance of approximately 8 Å correlating with enhanced luminescence efficiency. This prediction was experimentally validated through the synthesis of several OIMH compounds, demonstrating strong agreement between predicted and measured PLQY values. The machine learning approach not only enabled the screening of efficient compounds but also deepened the understanding of how structural factors, such as the structure of organic molecules, govern scintillation properties. These findings underscore the potential of machine learning in accelerating the development of next-generation luminescent materials with improved performance, offering a powerful tool for future material design and optimization.
期刊介绍:
The journal Chemistry of Materials focuses on publishing original research at the intersection of materials science and chemistry. The studies published in the journal involve chemistry as a prominent component and explore topics such as the design, synthesis, characterization, processing, understanding, and application of functional or potentially functional materials. The journal covers various areas of interest, including inorganic and organic solid-state chemistry, nanomaterials, biomaterials, thin films and polymers, and composite/hybrid materials. The journal particularly seeks papers that highlight the creation or development of innovative materials with novel optical, electrical, magnetic, catalytic, or mechanical properties. It is essential that manuscripts on these topics have a primary focus on the chemistry of materials and represent a significant advancement compared to prior research. Before external reviews are sought, submitted manuscripts undergo a review process by a minimum of two editors to ensure their appropriateness for the journal and the presence of sufficient evidence of a significant advance that will be of broad interest to the materials chemistry community.