{"title":"利用机器学习进行粉末 X 射线衍射分析以确定有机半导体晶体结构","authors":"Naoyuki Niitsu, Masato Mitani, Hiroyuki Ishii, Nobuhiko Kobayashi, Kenji Hirose, Shun Watanabe, Toshihiro Okamoto, Jun Takeya","doi":"10.1063/5.0208919","DOIUrl":null,"url":null,"abstract":"The crystal structure of organic semiconductors is an important factor that dominates various electronic properties, including charge transport properties. However, compared with the crystal structures of inorganic semiconductors, those of organic semiconductors are difficult to determine by powder x-ray diffraction (PXRD) analysis. Our proposed machine-learning (neural-network) technique can determine the diffraction peaks buried in noise and make deconvolution of the overlapped peaks of organic semiconductors, resulting in crystal-structure determination by the Rietveld analysis. As a demonstration, we apply the method to a few high-mobility organic semiconductors and confirm that the method is potentially useful for analyzing the crystal structure of organic semiconductors. The present method is also expected to be applicable to the determination of complex crystal structures in addition to organic semiconductors.","PeriodicalId":8094,"journal":{"name":"Applied Physics Letters","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Powder x-ray diffraction analysis with machine learning for organic-semiconductor crystal-structure determination\",\"authors\":\"Naoyuki Niitsu, Masato Mitani, Hiroyuki Ishii, Nobuhiko Kobayashi, Kenji Hirose, Shun Watanabe, Toshihiro Okamoto, Jun Takeya\",\"doi\":\"10.1063/5.0208919\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The crystal structure of organic semiconductors is an important factor that dominates various electronic properties, including charge transport properties. However, compared with the crystal structures of inorganic semiconductors, those of organic semiconductors are difficult to determine by powder x-ray diffraction (PXRD) analysis. Our proposed machine-learning (neural-network) technique can determine the diffraction peaks buried in noise and make deconvolution of the overlapped peaks of organic semiconductors, resulting in crystal-structure determination by the Rietveld analysis. As a demonstration, we apply the method to a few high-mobility organic semiconductors and confirm that the method is potentially useful for analyzing the crystal structure of organic semiconductors. The present method is also expected to be applicable to the determination of complex crystal structures in addition to organic semiconductors.\",\"PeriodicalId\":8094,\"journal\":{\"name\":\"Applied Physics Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-07-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Physics Letters\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1063/5.0208919\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Physics Letters","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1063/5.0208919","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, APPLIED","Score":null,"Total":0}
引用次数: 0
摘要
有机半导体的晶体结构是主导各种电子特性(包括电荷传输特性)的重要因素。然而,与无机半导体的晶体结构相比,有机半导体的晶体结构很难通过粉末 X 射线衍射(PXRD)分析来确定。我们提出的机器学习(神经网络)技术可以确定埋藏在噪声中的衍射峰,并对有机半导体的重叠峰进行解卷积,从而通过里特维尔德分析法确定晶体结构。作为示范,我们将该方法应用于几种高迁移率有机半导体,并证实该方法可用于分析有机半导体的晶体结构。除有机半导体外,本方法还有望适用于复杂晶体结构的测定。
Powder x-ray diffraction analysis with machine learning for organic-semiconductor crystal-structure determination
The crystal structure of organic semiconductors is an important factor that dominates various electronic properties, including charge transport properties. However, compared with the crystal structures of inorganic semiconductors, those of organic semiconductors are difficult to determine by powder x-ray diffraction (PXRD) analysis. Our proposed machine-learning (neural-network) technique can determine the diffraction peaks buried in noise and make deconvolution of the overlapped peaks of organic semiconductors, resulting in crystal-structure determination by the Rietveld analysis. As a demonstration, we apply the method to a few high-mobility organic semiconductors and confirm that the method is potentially useful for analyzing the crystal structure of organic semiconductors. The present method is also expected to be applicable to the determination of complex crystal structures in addition to organic semiconductors.
期刊介绍:
Applied Physics Letters (APL) features concise, up-to-date reports on significant new findings in applied physics. Emphasizing rapid dissemination of key data and new physical insights, APL offers prompt publication of new experimental and theoretical papers reporting applications of physics phenomena to all branches of science, engineering, and modern technology.
In addition to regular articles, the journal also publishes invited Fast Track, Perspectives, and in-depth Editorials which report on cutting-edge areas in applied physics.
APL Perspectives are forward-looking invited letters which highlight recent developments or discoveries. Emphasis is placed on very recent developments, potentially disruptive technologies, open questions and possible solutions. They also include a mini-roadmap detailing where the community should direct efforts in order for the phenomena to be viable for application and the challenges associated with meeting that performance threshold. Perspectives are characterized by personal viewpoints and opinions of recognized experts in the field.
Fast Track articles are invited original research articles that report results that are particularly novel and important or provide a significant advancement in an emerging field. Because of the urgency and scientific importance of the work, the peer review process is accelerated. If, during the review process, it becomes apparent that the paper does not meet the Fast Track criterion, it is returned to a normal track.