Activation entropy of dislocation glide in body-centered cubic metals from atomistic simulations

The activation entropy of dislocation glide, a key process controlling the strength of many metals, is often assumed to be constant or linked to enthalpy through the empirical Meyer-Neldel law-both of which are simplified approximations. In this study, we take a more direct approach by calculating the activation Gibbs energy for kink-pair nucleation on screw dislocations of two body-centered cubic metals, iron and tungsten. To ensure reliability, we develop machine learning interatomic potentials for both metals, carefully trained on dislocation data from density functional theory. Our findings reveal that dislocations undergo harmonic transitions between Peierls valleys, with an activation entropy that remains largely constant, regardless of temperature or applied stress. We use these results to parameterize a thermally-activated model of yield stress, which consistently matches experimental data in both iron and tungsten. Our work challenges recent studies using classical potentials, which report highly varying activation entropies, and suggests that simulations relying on classical potentials-widely used in materials modeling-could be significantly influenced by overestimated entropic effects.

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Publication Details

Journal
Nature Communications
Published
2025-09-24
DOI
https://doi.org/10.1038/s41467-025-62390-w
Citations
4
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
2.07

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article

Activation entropy of dislocation glide in body-centered cubic metals from atomistic simulations

David Rodney, Arnaud Allera, Michel Perez, Mihai‐Cosmin Marinica et al.
4 citations
Nature Communications
Machine Learning in Materials Science
2.07
article

Activation entropy of dislocation glide in body-centered cubic metals from atomistic simulations

David Rodney, Arnaud Allera, Michel Perez, Mihai‐Cosmin Marinica, Alexandra M. Goryaeva, Thomas D. Swinburne, Fabienne Ribeiro, Baptiste Bienvenu
article en
4 citations

Abstract

The activation entropy of dislocation glide, a key process controlling the strength of many metals, is often assumed to be constant or linked to enthalpy through the empirical Meyer-Neldel law-both of which are simplified approximations. In this study, we take a more direct approach by calculating the activation Gibbs energy for kink-pair nucleation on screw dislocations of two body-centered cubic metals, iron and tungsten. To ensure reliability, we develop machine learning interatomic potentials for both metals, carefully trained on dislocation data from density functional theory. Our findings reveal that dislocations undergo harmonic transitions between Peierls valleys, with an activation entropy that remains largely constant, regardless of temperature or applied stress. We use these results to parameterize a thermally-activated model of yield stress, which consistently matches experimental data in both iron and tungsten. Our work challenges recent studies using classical potentials, which report highly varying activation entropies, and suggests that simulations relying on classical potentials-widely used in materials modeling-could be significantly influenced by overestimated entropic effects.

Nature CommunicationsVol. 16(1)
Université Claude Bernard Lyon 1 (FR), Centre National de la Recherche Scientifique (FR), Aix-Marseille Université (FR), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), University of Michigan (US), Max-Planck-Institut für Nachhaltige Materialien (DE), CEA Cadarache (FR), Matériaux Ingénierie et Science (FR), CEA Paris-Saclay (FR), Institut Lumière Matière (FR), Centre d’Immunologie de Marseille-Luminy (FR), Centre Interdisciplinaire de Nanoscience de Marseille (FR), Institut National des Sciences Appliquées de Lyon (FR)
Agence Nationale de la Recherche, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Grand Équipement National De Calcul Intensif
Openalex Percentile: Top 12%
Machine Learning in Materials Science
2.07
DOIOpen Access PDFOpenAlex
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