Jaekak Yoo , Youngwoo Cho , Dong Hyeon Kim , Jaeseok Kim , Tae Geol Lee , Seung Mi Lee , Jaegul Choo , Mun Seok Jeong
{"title":"通过可解释人工智能揭示拉曼模式在评估氧化石墨烯还原程度中的作用","authors":"Jaekak Yoo , Youngwoo Cho , Dong Hyeon Kim , Jaeseok Kim , Tae Geol Lee , Seung Mi Lee , Jaegul Choo , Mun Seok Jeong","doi":"10.1016/j.nantod.2024.102366","DOIUrl":null,"url":null,"abstract":"<div><p>This paper evaluated the degree of reduction in graphene oxide, leveraging deep learning and machine learning models on over 15,000 Raman scattering spectra along with validation using density functional theory calculations. We addressed the limitations of previous studies, such as the consideration of an insufficient number of spectra as well as the lack of a comprehensive analysis of the contribution of individual Raman modes, by introducing machine learning and deep learning. Moreover, our models succeeded in predicting the carbon-to-oxygen ratio and classifying the reduction temperatures using the Raman scattering spectra as input. Employing the partial dependence plot and the feature importance, we interpreted the models and obtained consistent results on the significance of D* mode in graphene oxide. The intensity of the D* mode stands out by not only displaying the highest feature importance value for the reduction temperatures but also by correlating proportionally with the widest range of carbon-to-oxygen ratios among the various Raman modes in graphene oxide. Finally, we validated our findings through quantum mechanical calculations and confirmed the significance of the D* mode. Our study presents a comprehensive insight into the role of Raman modes in the degree of reduction as well as a precise methodology for evaluating the carbon-to-oxygen ratio of graphene oxide, a step towards its further industrial applications.</p></div>","PeriodicalId":395,"journal":{"name":"Nano Today","volume":null,"pages":null},"PeriodicalIF":13.2000,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unraveling the role of Raman modes in evaluating the degree of reduction in graphene oxide via explainable artificial intelligence\",\"authors\":\"Jaekak Yoo , Youngwoo Cho , Dong Hyeon Kim , Jaeseok Kim , Tae Geol Lee , Seung Mi Lee , Jaegul Choo , Mun Seok Jeong\",\"doi\":\"10.1016/j.nantod.2024.102366\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper evaluated the degree of reduction in graphene oxide, leveraging deep learning and machine learning models on over 15,000 Raman scattering spectra along with validation using density functional theory calculations. We addressed the limitations of previous studies, such as the consideration of an insufficient number of spectra as well as the lack of a comprehensive analysis of the contribution of individual Raman modes, by introducing machine learning and deep learning. Moreover, our models succeeded in predicting the carbon-to-oxygen ratio and classifying the reduction temperatures using the Raman scattering spectra as input. Employing the partial dependence plot and the feature importance, we interpreted the models and obtained consistent results on the significance of D* mode in graphene oxide. The intensity of the D* mode stands out by not only displaying the highest feature importance value for the reduction temperatures but also by correlating proportionally with the widest range of carbon-to-oxygen ratios among the various Raman modes in graphene oxide. Finally, we validated our findings through quantum mechanical calculations and confirmed the significance of the D* mode. Our study presents a comprehensive insight into the role of Raman modes in the degree of reduction as well as a precise methodology for evaluating the carbon-to-oxygen ratio of graphene oxide, a step towards its further industrial applications.</p></div>\",\"PeriodicalId\":395,\"journal\":{\"name\":\"Nano Today\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":13.2000,\"publicationDate\":\"2024-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nano Today\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1748013224002226\",\"RegionNum\":1,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nano Today","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1748013224002226","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Unraveling the role of Raman modes in evaluating the degree of reduction in graphene oxide via explainable artificial intelligence
This paper evaluated the degree of reduction in graphene oxide, leveraging deep learning and machine learning models on over 15,000 Raman scattering spectra along with validation using density functional theory calculations. We addressed the limitations of previous studies, such as the consideration of an insufficient number of spectra as well as the lack of a comprehensive analysis of the contribution of individual Raman modes, by introducing machine learning and deep learning. Moreover, our models succeeded in predicting the carbon-to-oxygen ratio and classifying the reduction temperatures using the Raman scattering spectra as input. Employing the partial dependence plot and the feature importance, we interpreted the models and obtained consistent results on the significance of D* mode in graphene oxide. The intensity of the D* mode stands out by not only displaying the highest feature importance value for the reduction temperatures but also by correlating proportionally with the widest range of carbon-to-oxygen ratios among the various Raman modes in graphene oxide. Finally, we validated our findings through quantum mechanical calculations and confirmed the significance of the D* mode. Our study presents a comprehensive insight into the role of Raman modes in the degree of reduction as well as a precise methodology for evaluating the carbon-to-oxygen ratio of graphene oxide, a step towards its further industrial applications.
期刊介绍:
Nano Today is a journal dedicated to publishing influential and innovative work in the field of nanoscience and technology. It covers a wide range of subject areas including biomaterials, materials chemistry, materials science, chemistry, bioengineering, biochemistry, genetics and molecular biology, engineering, and nanotechnology. The journal considers articles that inform readers about the latest research, breakthroughs, and topical issues in these fields. It provides comprehensive coverage through a mixture of peer-reviewed articles, research news, and information on key developments. Nano Today is abstracted and indexed in Science Citation Index, Ei Compendex, Embase, Scopus, and INSPEC.