First principles interatomic potential for tungsten based on Gaussian process regression
Wojciech Jerzy Szlachta

TL;DR
This paper develops a Gaussian process regression-based interatomic potential for tungsten, enabling accurate large-scale atomistic simulations of dislocations and defects that align with quantum mechanical results.
Contribution
It introduces a GAP model for tungsten using SOAP descriptors, capable of accurately modeling dislocation behavior and defect interactions at scales beyond traditional quantum methods.
Findings
GAP potential accurately models screw dislocation in tungsten
Dislocation mobility and Peierls barrier computed with QM-level accuracy
Model enables simulations with over 100,000 atoms
Abstract
An accurate description of atomic interactions, such as that provided by first principles quantum mechanics, is fundamental to realistic prediction of the properties that govern plasticity, fracture or crack propagation in metals. However, the computational complexity associated with modern schemes explicitly based on quantum mechanics limits their applications to systems of a few hundreds of atoms at most. This thesis investigates the application of the Gaussian Approximation Potential (GAP) scheme to atomistic modelling of tungsten - a bcc transition metal which exhibits a brittle-to-ductile transition and whose plasticity behaviour is controlled by the properties of screw dislocations. We apply Gaussian process regression to interpolate the quantum-mechanical (QM) potential energy surface from a set of points in atomic configuration space. Our…
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Taxonomy
TopicsMachine Learning in Materials Science · Advanced Materials Characterization Techniques · X-ray Diffraction in Crystallography
