Mechanism of sluggish diffusion under rough energy landscape

Biao Xu; Jun Zhang; Yaoxu Xiong; Shihua Ma; Yuri Osetsky; Shijun Zhao

doi:10.1016/j.xcrp.2023.101337

Mechanism of sluggish diffusion under rough energy landscape

Biao Xu, Jun Zhang, Yaoxu Xiong, Shihua Ma, Yuri Osetsky, Shijun Zhao

Microstructural Evolution Modeling

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

High-entropy alloys (HEAs) are a new class of metallic materials that demonstrate potentially very useful functional and structural properties. Sluggish diffusion, one of the core effects responsible for their exotic properties, has been intensively debated. Here, we demonstrate that a combination of machine learning (ML) and kinetic Monte Carlo (kMC) can uncover the complicated links between the rough potential energy landscape (PEL) and atomic transport in HEAs. The ML model accurately represents the local environment dependence of PEL, and the developed ML-kMC allows us to reach the timescale required to reveal how composition-dependent PEL governs self-diffusion in HEAs. We further delineate a species-resolved analytical diffusion model that can capture essential features of self-diffusion in arbitrary alloy composition and temperature in HEAs. This work elucidates the governing mechanism for sluggish diffusion in HEAs, which enables efficient and accurate manipulation of diffusion properties in HEAs by tailoring alloy composition and corresponding PEL.

Original language	English
Article number	101337
Journal	Cell Reports Physical Science
Volume	4
Issue number	4
DOIs	https://doi.org/10.1016/j.xcrp.2023.101337
State	Published - Apr 19 2023

Funding

This work was supported by the National Natural Science Foundation of China (no. 11975193 ), Research Grant Council of Hong Kong (nos. 11200421 and C1017-21G ), and Hong Kong Innovation and Technology Commission (no. MHP/098/21 ). Y.O.’s research was supported by the Energy Dissipation to Defect Evolution (EDDE), an Energy Frontier Research Center funded by the U.S. Department of Energy , Office of Science, Basic Energy Sciences under contract number DE-AC05- 00OR22725 . This manuscript has been co-authored by UT–Battelle, LLC under Contract No. DE–AC05–00OR22725 with the U.S. Department of Energy. The United States government retains and the publisher, by accepting the article for publication, acknowledges that the United States government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan.

Funders	Funder number
U.S. Department of Energy
Battelle	DE–AC05–00OR22725
Office of Science
Basic Energy Sciences	DE-AC05- 00OR22725
National Natural Science Foundation of China	11975193
Research Grants Council, University Grants Committee	C1017-21G, 11200421
Innovation and Technology Commission - Hong Kong	MHP/098/21

Keywords

atomistic simulation
diffusion
energy landscape
high-entropy alloys
kinetic Monte Carlo
local atomic enviroment
machine learning
migration barriers
self diffusion
sluggish diffusion
tracer diffusion

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10.1016/j.xcrp.2023.101337

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title = "Mechanism of sluggish diffusion under rough energy landscape",

abstract = "High-entropy alloys (HEAs) are a new class of metallic materials that demonstrate potentially very useful functional and structural properties. Sluggish diffusion, one of the core effects responsible for their exotic properties, has been intensively debated. Here, we demonstrate that a combination of machine learning (ML) and kinetic Monte Carlo (kMC) can uncover the complicated links between the rough potential energy landscape (PEL) and atomic transport in HEAs. The ML model accurately represents the local environment dependence of PEL, and the developed ML-kMC allows us to reach the timescale required to reveal how composition-dependent PEL governs self-diffusion in HEAs. We further delineate a species-resolved analytical diffusion model that can capture essential features of self-diffusion in arbitrary alloy composition and temperature in HEAs. This work elucidates the governing mechanism for sluggish diffusion in HEAs, which enables efficient and accurate manipulation of diffusion properties in HEAs by tailoring alloy composition and corresponding PEL.",

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AU - Xu, Biao

AU - Zhang, Jun

AU - Xiong, Yaoxu

AU - Ma, Shihua

AU - Osetsky, Yuri

AU - Zhao, Shijun

PY - 2023/4/19

Y1 - 2023/4/19

N2 - High-entropy alloys (HEAs) are a new class of metallic materials that demonstrate potentially very useful functional and structural properties. Sluggish diffusion, one of the core effects responsible for their exotic properties, has been intensively debated. Here, we demonstrate that a combination of machine learning (ML) and kinetic Monte Carlo (kMC) can uncover the complicated links between the rough potential energy landscape (PEL) and atomic transport in HEAs. The ML model accurately represents the local environment dependence of PEL, and the developed ML-kMC allows us to reach the timescale required to reveal how composition-dependent PEL governs self-diffusion in HEAs. We further delineate a species-resolved analytical diffusion model that can capture essential features of self-diffusion in arbitrary alloy composition and temperature in HEAs. This work elucidates the governing mechanism for sluggish diffusion in HEAs, which enables efficient and accurate manipulation of diffusion properties in HEAs by tailoring alloy composition and corresponding PEL.

AB - High-entropy alloys (HEAs) are a new class of metallic materials that demonstrate potentially very useful functional and structural properties. Sluggish diffusion, one of the core effects responsible for their exotic properties, has been intensively debated. Here, we demonstrate that a combination of machine learning (ML) and kinetic Monte Carlo (kMC) can uncover the complicated links between the rough potential energy landscape (PEL) and atomic transport in HEAs. The ML model accurately represents the local environment dependence of PEL, and the developed ML-kMC allows us to reach the timescale required to reveal how composition-dependent PEL governs self-diffusion in HEAs. We further delineate a species-resolved analytical diffusion model that can capture essential features of self-diffusion in arbitrary alloy composition and temperature in HEAs. This work elucidates the governing mechanism for sluggish diffusion in HEAs, which enables efficient and accurate manipulation of diffusion properties in HEAs by tailoring alloy composition and corresponding PEL.

KW - atomistic simulation

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KW - energy landscape

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KW - migration barriers

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KW - tracer diffusion

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Mechanism of sluggish diffusion under rough energy landscape

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Keywords

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