Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses-Reference-Cited by-同舟云学术

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Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses

Published:2019-06 Issue:2 Volume:9 Page:576-585
ISSN:2159-6859
Container-title:MRS Communications
language:en
Short-container-title:MRS Communications

Author:

Xiong Jie,Zhang Tong-Yi,Shi San-Qiang

Funder

Research grants from the Science and Technology Commission of Shanghai Municipality

111 Project

PolyU internal grants

Publisher

Springer Science and Business Media LLC

Subject

General Materials Science

Link

http://link.springer.com/content/pdf/10.1557/mrc.2019.44.pdf

Reference27 articles.

1. W.H. Wang: The elastic properties, elastic models and elastic perspectives of metallic glasses. Prog. Mater. Sci. 57, 487–656 (2012).

2. R. Ramprasad, R. Batra, G. Pilania, A. Mannodi-Kanakkithodi, and C. Kim: Machine learning in materials informatics: Recent applications and prospects. NPJ Comput. Mater. 3, 54 (2017).

3. P. Raccuglia, K.C. Elbert, P.D.F. Adler, C. Falk, M.B. Wenny, A. Mollo, M. Zeller, S.A. Friedler, J. Schrier, and A.J. Norquist: Machine-learningassisted materials discovery using failed experiments. Nature 533, 73–76 (2016).

4. S. Ramakrishna, T.Y. Zhang, W.C. Lu, Q. Qian, J.S.C. Low, J.H.R. Yune, D.Z.L. Tan, S. Bressan, S. Sanvito, and S.R. Kalidindi: Materials informatics. J. Intell. Manuf. 29, 1–20 (2018).

5. Y.T. Sun, H.Y. Bai, M.Z. Li, and W.H. Wang: Machine learning approach for prediction and understanding of glass-forming ability. J. Phys. Chem. Lett. 8, 3434–3439 (2017).

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