Page 49 - Read Online

Liu et al. J Mater Inf 2022;2:20 Journal of
DOI: 10.20517/jmi.2022.29
Materials Informatics

Research Article Open Access

Identifying stress-induced heterogeneity in
Cu Zr Ni Ti Pd high-entropy metallic glass from
20
20
20
20
20
machine learning atomic dynamics
1
1
1,2
Xiaodi Liu , Wenfei Lu , Wenkang Tu , Jun Shen 1,*
1
College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, Guangdong, China.
2
School of Materials Science and Engineering, Tongji University, Shanghai 201804, China.
* Correspondence to: Prof. Jun Shen, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai
Road, Shenzhen 518060, Guangdong, China. E-mail: junshen@szu.edu.cn
How to cite this article: Liu X, Lu W, Tu W, Shen J. Identifying stress-induced heterogeneity in Cu Zr Ni Ti Pd high-entropy
20 20 20 20 20
metallic glass from machine learning atomic dynamics. J Mater Inf 2022;2:20. https://dx.doi.org/10.20517/jmi.2022.29
Received: 27 Sep 2022 First Decision: 27 Oct 2022 Revised: 14 Nov 2022 Accepted: 7 Dec 2022 Published: 22 Dec 2022

Academic Editors: Xingjun Liu, Wen Chen Copy Editor: Ke-Cui Yang Production Editor: Ke-Cui Yang

Abstract
High-entropy metallic glasses (HEMGs) are amorphous alloys with a near-equiatomic composition containing at
least five elements. Such a unique non-crystalline structure with high configurational entropy of mixing provides
HEMGs with promising prospects in applications, and it also attracts great scientific interest. In this paper, we
focused on the atomic mechanism of stress-induced heterogeneity in the Cu Zr Ni Ti Pd HEMG. Applying the
20 20 20 20 20
machine learning (ML) technique combined with the classical molecular dynamics (MD) simulation, we defined
the liquid-like active atoms as the ones exhibiting high machine-learned temperature (T ). T is a parameter to
ML ML
characterize the atomic motion activated by thermal and mechanical stimuli. The results reveal the stress-induced
heterogeneity in atomic dynamics during creep. Local plastic flows originate from these active “hot” atoms, which
have low five-fold symmetry, low coordination packing, and obvious chemical short-range ordering. Compared with
conventional metallic glasses (MGs), the HEMG exhibits a smaller activation volume of creep, fewer active atoms,
and sluggish dynamics. The results provide physical insights into the structural and dynamic heterogeneity in
HEMGs at an atomic level.

Keywords: High-entropy metallic glasses, machine learning, k-nearest neighbor, molecular dynamics simulation,
creep

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.

www.jmijournal.com