Machine-learning potentials for structurally and chemically complex MAB phases: Strain hardening and ripplocation-mediated plasticity

Machine-learning potentials for structurally and chemically complex MAB phases: Strain hardening and ripplocation-mediated plasticity

Though offering unprecedented pathways to molecular dynamics (MD) simulations of technologically-relevant materials and conditions, machine-learning interatomic potentials (MLIPs) are typically trained for “simple” materials and properties with minor size effects. Our study of MAB phases (MABs)—alte...

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Bibliographic Details
Main Authors: Nikola Koutná, Shuyao Lin, Lars Hultman, Davide G. Sangiovanni, Paul H. Mayrhofer
Format: Article
Language:English
Published: Elsevier 2025-08-01
Series:Materials & Design
Subjects:
MAB phase
Machine-learning interatomic potentials
Mechanical properties
Molecular dynamics
Online Access:http://www.sciencedirect.com/science/article/pii/S0264127525007270
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