Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability-Reference-Cited by-同舟云学术

Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability

Published:2017-07-12 Issue:14 Volume:8 Page:3434-3439
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Sun Y. T.¹²^ORCID,Bai H. Y.¹²,Li M. Z.³,Wang W. H.¹²

Affiliation:

1. Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China

2. University of Chinese Academy of Science, Beijing 100049, People’s Republic of China

3. Department of Physics, Beijing Key Laboratory of Optoelectronic Functional Materials & Micro-nano Devices, Renmin University of China, Beijing 100872, People’s Republic of China

Funder

Ministry of Science and Technology of the People's Republic of China

National Natural Science Foundation of China

Chinese Academy of Sciences

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.7b01046

Reference34 articles.

1. Non-crystalline Structure in Solidified Gold–Silicon Alloys

2. Metallic glasses as structural materials

3. Bulk Metallic Glasses with Functional Physical Properties

4. Bulk metallic glasses

5. Formation of monatomic metallic glasses through ultrafast liquid quenching

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