进化材料基因组:机器学习如何推动下一代材料发现

IF 10.6 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
C. Suh, Clyde Fare, J. Warren, Edward O. Pyzer-Knapp
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引用次数: 44

摘要

应用于化学和材料数据的机器学习正在改变材料发现和设计领域,但充分利用机器学习算法、工具和方法仍需要大量工作。在这里,我们回顾了迄今为止社区的成就,并评估了结合材料科学和化学观点的最先进的数据密集型研究活动的成熟度。我们关注三个主要主题——学习观察、学习估计和学习搜索材料——以展示先进的计算学习技术如何快速成功地用于解决材料和化学问题。此外,我们还讨论了一条通往未来的清晰道路,在未来,数据驱动的材料发现和设计方法将成为标准实践。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery
Machine learning, applied to chemical and materials data, is transforming the field of materials discovery and design, yet significant work is still required to fully take advantage of machine learning algorithms, tools, and methods. Here, we review the accomplishments to date of the community and assess the maturity of state-of-the-art, data-intensive research activities that combine perspectives from materials science and chemistry. We focus on three major themes—learning to see, learning to estimate, and learning to search materials—to show how advanced computational learning technologies are rapidly and successfully used to solve materials and chemistry problems. Additionally, we discuss a clear path toward a future where data-driven approaches to materials discovery and design are standard practice.
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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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