Machine learning and quantum theory of magnets for energy efficient and renewable energy technologies

The project is closely connected to ongoing research being conducted in collaboration with HetSys partner Forschungszentrum Julich. The project will also enhance the collaboration which has just begun with materials scientists at Northeastern University in Boston in the USA on developing novel materials processing for magneto-functional materials. There will be opportunities for the student to visit both partner institutes.

Magnetic materials are technologically indispensable - used in motors, generators, solid state cooling, electronic devices, data storage, medical treatment, toys etc. Although the effects of magnetism are easily understood on the macroscopic scale, it has its origins in the complex collective behaviour of the electronic glue, simultaneously binding the nuclei of the material together and generating magnetic moments. In this project we will identify atomistic, classical spin models by using machine learning tools on data from calculations of the fundamental quantum mechanics of the electrons. From their study we will discover ways to design new magnets with reduced amounts of critical elements such as rare earth metals. The work will relate directly to theoretical work and experimental measurements by International Partners.

With the drive towards more energy efficient technologies, renewable energy supplies and further miniaturisation of devices, there is an urgent demand for stronger and cheaper magnetic materials. This project will be part of ongoing development of computational modelling to understand intrinsic magnetic properties, to refine design principles and to aid the search for new functional magnets. A magnetic material comprises a crystalline lattice of nuclei surrounded by a glue of septillions of interacting electrons. Moreover, the same electrons which underpin the magnetism of a material are also responsible for determining the arrangements of its atoms. The complexity of this electron fluid presents a fundamental challenge for theory and computational modelling - the magnetism it can lead to comes from composite spins coalescing around atomic sites as a result of the cooperative behaviour of many electrons. There are interactions between pairs of such classical spins and among clusters of them. In principle these multi-spin parameters can be determined from calculations of the fundamental quantum mechanics of the electrons. To date we have developed a cluster expansion of the free energy of the system in terms of the quantities which describe the average order of the spins around the atomic sites, i.e. local magnetic order parameters, and we can describe accurately many magnetic properties and how they vary with temperature, composition and applied fields.

The extraction and investigation of an accurate model classical spin-Hamiltonian, however, from such ab initio data is a challenging task and it is at the heart of this project. To take the work to the next level and enable it to describe multicomponent magnetic materials for the design of new magnets with reduced levels of critical elements as well as materials with intriguing topological magnetic structures (skyrmions) we need to develop machine learning tools to determine the form of the free energy rather than our current ad hoc approach.

This work will also enhance our modelling of how arrangements of atoms in multi-component alloys can be affected by the application of strain and magnetic fields and hence have an impact on novel materials processing being developed by collaborators.

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.