Correlation analysis of materials properties by machine learning: illustrated with stacking fault energy from first-principles calculations in dilute fcc-based alloys-Reference-Cited by-同舟云学术

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Correlation analysis of materials properties by machine learning: illustrated with stacking fault energy from first-principles calculations in dilute fcc-based alloys

Published:2021-06-14 Issue:29 Volume:33 Page:295702
ISSN:0953-8984
Container-title:Journal of Physics: Condensed Matter
language:
Short-container-title:J. Phys.: Condens. Matter

Author:

Chong Xiaoyu,Shang Shun-Li^ORCID,Krajewski Adam M^ORCID,Shimanek John D^ORCID,Du Weihang,Wang Yi,Feng Jing,Shin Dongwon,Beese Allison M^ORCID,Liu Zi-Kui^ORCID

Funder

Rare and Precious Metals Material Genetic Engineering Project of Yunnan Province, 2020

US Department of Energy

Publisher

IOP Publishing

Subject

Condensed Matter Physics,General Materials Science

Link

https://iopscience.iop.org/article/10.1088/1361-648X/ac0195/pdf

Reference83 articles.

1. Machine learning in materials informatics: recent applications and prospects;Ramprasad;npj Comput. Mater.,2017

2. Machine learning in materials science;Mueller,2016

3. Machine learning for molecular and materials science;Butler;Nature,2018

4. Recent advances and applications of machine learning in solid-state materials science;Schmidt;npj Comput. Mater.,2019

5. A general-purpose machine learning framework for predicting properties of inorganic materials;Ward;npj Comput. Mater.,2016

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