Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches

IF 10.6 2区材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

Annual Review of Materials Research Pub Date : 2020-07-01 DOI:10.1146/annurev-matsci-090319-010954

J. Saal, A. Oliynyk, B. Meredig

引用次数: 68

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...

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材料发现中的机器学习:已证实的预测及其基本方法

对机器学习(ML)用于材料发现的兴趣迅速增长，导致了大量已发表的工作。然而，这些出版物中只有一小部分包括证实……

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来源期刊

Annual Review of Materials Research 工程技术-材料科学：综合

CiteScore

17.70

自引率

1.00%

发文量

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.