{"title":"材料发现中的机器学习:已证实的预测及其基本方法","authors":"J. Saal, A. Oliynyk, B. Meredig","doi":"10.1146/annurev-matsci-090319-010954","DOIUrl":null,"url":null,"abstract":"The rapidly growing interest in machine learning (ML) for materials discovery has resulted in a large body of published work. However, only a small fraction of these publications includes confirmat...","PeriodicalId":8055,"journal":{"name":"Annual Review of Materials Research","volume":null,"pages":null},"PeriodicalIF":10.6000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"68","resultStr":"{\"title\":\"Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches\",\"authors\":\"J. Saal, A. Oliynyk, B. Meredig\",\"doi\":\"10.1146/annurev-matsci-090319-010954\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The rapidly growing interest in machine learning (ML) for materials discovery has resulted in a large body of published work. However, only a small fraction of these publications includes confirmat...\",\"PeriodicalId\":8055,\"journal\":{\"name\":\"Annual Review of Materials Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":10.6000,\"publicationDate\":\"2020-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"68\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annual Review of Materials Research\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1146/annurev-matsci-090319-010954\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual Review of Materials Research","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1146/annurev-matsci-090319-010954","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches
The rapidly growing interest in machine learning (ML) for materials discovery has resulted in a large body of published work. However, only a small fraction of these publications includes confirmat...
期刊介绍：
The Annual Review of Materials Research, published since 1971, is a journal that covers significant developments in the field of materials research. It includes original methodologies, materials phenomena, material systems, and special keynote topics. The current volume of the journal has been converted from gated to open access through Annual Reviews' Subscribe to Open program, with all articles published under a CC BY license. The journal defines its scope as encompassing significant developments in materials science, including methodologies for studying materials and materials phenomena. It is indexed and abstracted in various databases, such as Scopus, Science Citation Index Expanded, Civil Engineering Abstracts, INSPEC, and Academic Search, among others.
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