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Spanning the scales: insights into dislocation mobility provided by machine learning and coarse-grained models


Supervisors: Albert Bartok-Partay, James Kermode, Tom Hudson

Student: Matt Nutter

How do metals break? How can we make them stronger? What are the roles of defects and impurities? The strength of materials are ultimately determined by the microscopic interactions on the atomic level, which can be modelled accurately. However, the challenge is that computationally it is not possible to propagate information in one step from the nanometer to the millimeter scale. In this project, you will use combined Quantum Mechanics-Molecular Mechanics and Gaussian Approximation Potentials, a machine learning approach, to develop coarse-grain models of dislocations and to make quantitative predictions of plastic deformations in metals and alloys.

The stress generated in a metal resisting plastic deformation is governed by the dislocation mobility, the ease by which dislocations move through the crystal. Dislocation motion is limited by the processes of formation and migration of kinks and pinning by defects. Modelling of these processes on the atomistic scale has been carried out for fast-moving dislocations under large stresses, corresponding to conditions in the rise of a shock wave, but at lower strain-rates, timescales are too long to access using atomistic methods.

Coarse grained methods bypass the need to model the atoms explicitly, determining the dislocation mobility from quantities such as the kink-pair activation enthalpy using statistical mechanics. A multiscale approach is needed to reveal the details of the structure and energetics of dislocations, including the dislocation kink structure and dependencies on non-glide stress components, as well as providing inputs for improved coarse-grained models.

In collaboration with AWE, this project will explore the link between dislocation energetics and dislocation mobility, through the development of coarse-grained models for screw dislocation mobility in bcc metals, including the effect of non-glide stresses. Machine-learning based interatomic potentials and QM/MM approaches will be used to span the gap between the capabilities of ab initio modelling and the required time and length scales. In addition to studying the pure metal, the effect of impurities will be investigated. Small interstitial impurities and larger substitutional alloying will be considered. Building on prior work, W will be considered initially. There is scope for considering other systems such as Fe and steel, Ta, TaW, or V. Validation of the coarse-grained dislocation model in the strongly driven regime will be sought by comparing with direct molecular dynamics simulations of dislocation mobility. Investigation of how the coarse-grained model will be validated in the weakly driven regime will be pursued.