CUED Publications database

Machine-learning based potential for iron: Plasticity and phase transition

Maillet, JB and Denoual, C and Csányi, G (2018) Machine-learning based potential for iron: Plasticity and phase transition. In: UNSPECIFIED.

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Abstract

© 2018 Author(s). A classical interatomic potential is trained within the GAP framework with the goal of reproducing both plastic properties and phase transition for iron. We first build a reference compact database based on tight-binding calculations of the ferromagnetic bcc and antiferromagnetic hcp phase, as well as the transition path between the two structures. We then show how the GAP formalism enables the reproduction of diverse properties that include equation of state, elasticity, plasticity, and phase transition for this complex system.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div C > Applied Mechanics
Depositing User: Cron Job
Date Deposited: 02 Oct 2018 01:50
Last Modified: 02 Mar 2021 08:06
DOI: 10.1063/1.5044794