{"title":"材料科学中机器学习的机遇与挑战","authors":"D. Morgan, R. Jacobs","doi":"10.1146/annurev-matsci-070218-010015","DOIUrl":null,"url":null,"abstract":"Advances in machine learning have impacted myriad areas of materials science, such as the discovery of novel materials and the improvement of molecular simulations, with likely many more important developments to come. Given the rapid changes in this field, it is challenging to understand both the breadth of opportunities and the best practices for their use. In this review, we address aspects of both problems by providing an overview of the areas in which machine learning has recently had significant impact in materials science, and then we provide a more detailed discussion on determining the accuracy and domain of applicability of some common types of machine learning models. Finally, we discuss some opportunities and challenges for the materials community to fully utilize the capabilities of machine learning.","PeriodicalId":8055,"journal":{"name":"Annual Review of Materials Research","volume":"92 1","pages":""},"PeriodicalIF":10.6000,"publicationDate":"2020-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"162","resultStr":"{\"title\":\"Opportunities and Challenges for Machine Learning in Materials Science\",\"authors\":\"D. Morgan, R. Jacobs\",\"doi\":\"10.1146/annurev-matsci-070218-010015\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Advances in machine learning have impacted myriad areas of materials science, such as the discovery of novel materials and the improvement of molecular simulations, with likely many more important developments to come. Given the rapid changes in this field, it is challenging to understand both the breadth of opportunities and the best practices for their use. In this review, we address aspects of both problems by providing an overview of the areas in which machine learning has recently had significant impact in materials science, and then we provide a more detailed discussion on determining the accuracy and domain of applicability of some common types of machine learning models. Finally, we discuss some opportunities and challenges for the materials community to fully utilize the capabilities of machine learning.\",\"PeriodicalId\":8055,\"journal\":{\"name\":\"Annual Review of Materials Research\",\"volume\":\"92 1\",\"pages\":\"\"},\"PeriodicalIF\":10.6000,\"publicationDate\":\"2020-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"162\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annual Review of Materials Research\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1146/annurev-matsci-070218-010015\",\"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-070218-010015","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}
Opportunities and Challenges for Machine Learning in Materials Science
Advances in machine learning have impacted myriad areas of materials science, such as the discovery of novel materials and the improvement of molecular simulations, with likely many more important developments to come. Given the rapid changes in this field, it is challenging to understand both the breadth of opportunities and the best practices for their use. In this review, we address aspects of both problems by providing an overview of the areas in which machine learning has recently had significant impact in materials science, and then we provide a more detailed discussion on determining the accuracy and domain of applicability of some common types of machine learning models. Finally, we discuss some opportunities and challenges for the materials community to fully utilize the capabilities of machine learning.
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.