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Semantic Scholar Open Access 2020 190 sitasi

Machine learning in materials genome initiative: A review

Yingli Liu Chen Niu Zhuo Wang Yong-Liang Gan Yan Zhu +2 lainnya
Lihat Sumber DOI

Abstrak

Abstract Discovering new materials with excellent performance is a hot issue in the materials genome initiative. Traditional experiments and calculations often waste large amounts of time and money and are also limited by various conditions. Therefore, it is imperative to develop a new method to accelerate the discovery and design of new materials. In recent years, material discovery and design methods using machine learning have attracted much attention from material experts and have made some progress. This review first outlines available materials database and material data analytics tools and then elaborates on the machine learning algorithms used in materials science. Next, the field of application of machine learning in materials science is summarized, focusing on the aspects of structure determination, performance prediction, fingerprint prediction, and new material discovery. Finally, the review points out the problems of data and machine learning in materials science and points to future research. Using machine learning algorithms, the authors hope to achieve amazing results in material discovery and design.

Topik & Kata Kunci

Materials Science

Penulis (7)

Y

Yingli Liu

C

Chen Niu

Z

Zhuo Wang

Y

Yong-Liang Gan

Y

Yan Zhu

S

Shuhong Sun

T

Tao Shen

Format Sitasi

APA MLA BibTeX
Liu, Y., Niu, C., Wang, Z., Gan, Y., Zhu, Y., Sun, S. et al. (2020). Machine learning in materials genome initiative: A review. https://doi.org/10.1016/j.jmst.2020.01.067

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Informasi Jurnal
Tahun Terbit
2020
Bahasa
en
Total Sitasi
190×
Sumber Database
Semantic Scholar
DOI
10.1016/j.jmst.2020.01.067
Akses
Open Access ✓