Industrial materials informatics: Analyzing large-scale data to solve applied problems in R&D, manufacturing, and supply chain

被引：27

作者：

Meredig, Bryce ^{[1
]}

机构：

[1] Citrine Informat, Redwood City, CA 94063 USA

来源：

CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE | 2017年 / 21卷 / 03期

关键词：

Materials informatics; Materials data; Databases; Machine learning; Analytics; Manufacturing; DENSITY-FUNCTIONAL THEORY; NEURAL-NETWORKS; DISCOVERY; GE;

D O I：

10.1016/j.cossms.2017.01.003

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this review, we discuss current and potential future applications for materials informatics in industry. We include in this discussion not only the traditional materials and chemical industries, but also other manufacturing-intensive sectors, which broadens the relevance of materials informatics to a large proportion of the economy. We describe several high-level use cases, drawing upon our experience at Citrine Informatics working in materials and manufacturing, although we omit any details that could be considered customer-proprietary. We note that a converging set of factors, including executive level corporate demand for Big Data technologies, increasing availability of large-scale materials data, drive for greater competitiveness in manufacturing, and advances in machine learning, will lead to a rapid increase in industrial application of materials informatics over the next several years. (C) 2017 Published by Elsevier Ltd.

引用

页码：159 / 166

页数：8

共 61 条

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