Mechanism of sluggish diffusion under rough energy landscape

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number101337
Journal / PublicationCell Reports Physical Science
Volume4
Issue number4
Online published21 Mar 2023
Publication statusPublished - 19 Apr 2023

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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. © 2023 The Author(s)

Research Area(s)

  • 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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