Accelerating the prediction of stacking fault energy by combining <i>ab initio</i> calculations and machine learning-Reference-Cited by-同舟云学术

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Accelerating the prediction of stacking fault energy by combining ab initio calculations and machine learning

Published:2024-06-03 Issue:21 Volume:109 Page:
ISSN:2469-9950
Container-title:Physical Review B
language:en
Short-container-title:Phys. Rev. B

Author:

Linda Albert¹^ORCID,Akhtar Md. Faiz¹^ORCID,Pathak Shaswat²^ORCID,Bhowmick Somnath¹^ORCID

Affiliation:

1. Indian Institute of Technology Kanpur

2. SRM College of Engineering and Technology

Funder

Department of Science and Technology, Ministry of Science and Technology, India

Indian Institute of Technology Kanpur

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevB.109.214102/fulltext

Reference82 articles.

1. Effect of temperature on the stacking fault energy and deformation behaviour in 316L austenitic stainless steel

2. Temperature dependence of the stacking-fault Gibbs energy for Al, Cu, and Ni

3. Stress-dependence of generalized stacking fault energies

4. Effect of pressure on stacking fault energy and deformation behavior of face-centered cubic metals

5. Effects of solute concentration on the stacking fault energy in copper alloys at finite temperatures

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1. Accelerating the prediction of stacking fault energy by combining ab initio calculations and machine learning;Physical Review B;2024-06-03

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