Opportunities and Challenges for Machine Learning in Materials Science

IF 10.6 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY Annual Review of Materials Research Pub Date : 2020-06-25 DOI:10.1146/annurev-matsci-070218-010015
D. Morgan, R. Jacobs
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引用次数: 162

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.
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材料科学中机器学习的机遇与挑战
机器学习的进步已经影响了材料科学的无数领域,比如新材料的发现和分子模拟的改进,未来可能会有更多重要的发展。考虑到该领域的快速变化,了解机会的广度和最佳使用实践是一项挑战。在这篇综述中,我们通过概述机器学习最近在材料科学中产生重大影响的领域来解决这两个问题,然后我们对确定一些常见类型的机器学习模型的准确性和适用性领域进行了更详细的讨论。最后,我们讨论了材料界充分利用机器学习能力的一些机遇和挑战。
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来源期刊
Annual Review of Materials Research
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
21
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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