Spanning the Scales: Insights into Dislocation Mobility Provided by Machine Learning and Coarse-Grained Models

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.

Impact on Students. The primary impact will be on the 50+ PhD students trained by the Centre. They will be high-quality computational scientists who can develop and implement new methods for modelling complex systems in collaboration with scientists and end-users, who are comfortable working in interdisciplinary environments, have excellent communication skills and be well prepared for a wide range of future careers. The students will tackle and disseminate results from exciting PhD projects with strong potential for direct impact. Exemplar research themes we have identified together with our industrial and international partners: (i) design of electronic devices, (ii) catalysis across scales, (iii) high-performance alloys, (iv) direct drive laser fusion, (v) future medicine exploration, (vi) smart nanofluidic interfaces, (vii) composite materials with enhanced functionality, (viii) heterogeneity of underground systems.

Impact on Industry. Students trained by HetSys will make a significant impact on UK industry as they will be ideally prepared for R&D careers to help to address the skills shortage in science and engineering. They will be in high demand for their ability to (i) work across disciplines, (ii) perform calculations that come along with error estimates, and (iii) develop well-designed software that other researchers can readily use and modify which implements novel solutions to scientific problems. More generally, incorporating error bars into models to take account of incomplete data and insufficient models could lead to significantly enhanced adoption of materials modelling in industry, reducing trial and error, and costly/time-consuming R&D procedures. The global market for simulation software is expected to more than double from now to 2022 indicating a very strong absorptive capacity for graduates. Moreover, a recent European Materials Modelling Consortium report identified a typical eight-fold return on investment for materials modelling research, leading to cost savings of 12M Euros per industrial project.

Impact on Society. Scarcity of resources and high energy requirements of traditional materials processing techniques raise ever-increasing sustainability concerns. Limitations on jet engine fuel efficiency and the difficulties of designing materials for fusion power stations reflect the social and economic cost of our incomplete knowledge of how complex heterogeneous systems behave. High costs of laboratory investigations mean that theory must aid experiment to produce new knowledge and guidance. By training students who can develop the new methodology needed to model such issues, HetSys will support society's long term need for improved materials and processes.

There will also be a direct impact locally and regionally through engagement by HetSys in outreach projects. For example we will encourage CDT students to be involved with annual 'Inspire' residential courses at Warwick for Year 11 girls, which will show what STEM subjects are like at degree level. CDT students will present highlights from projects to secondary-school pupils during these courses and also visit local schools, particularly in areas currently under-represented in the student body, in coordination with relevant professional bodies.

Impact on collaboration. Our international partners have identified the same urgent challenges for computational modelling. We will build flourishing links with research institutes abroad with long term benefit on UK research via our links to computational science networks. Shared research projects will strengthen links between academic staff and industry R&D personnel and across disciplines. The work will also lead to accessible, robust and reusable software. The Centre will achieve cross-disciplinary academic impact on the physical and materials sciences, engineering, manufacturing and mathematics communities at Warwick and beyond, and on the generation of new ideas, insights and techniques.