Maximizing information content in neutron reflectivity data by optimised magnetic reference layers

We will use our combined expertise on growth and characterization of magnetic layers to develop new methodology for exploring soft matter and life sciences using neutron scattering.

The project is truly cross disciplinary and will utilise artificial intelligence in both the design of the experiments and analysis of data.

The work will be done in close collaboration with researchers at both ISIS and ESS, with the intention is to build on existing competence and infrastructure, ensuring successful use of the developed methodology.The main focus will be on the development of new amorphous magnetic alloys and by developing a machine learning algoritm for the design of nuclear and magnetic scattering lengths in thin well-defined structures we will maximise the information content in multiple independent spin-polarised neutron reflectivity data obtained by three different magnetisation - neutron spin relationships.

Software for coupled simulations will also be developed.

This in combination with the development of seeding and capping layers, will be the base for producing and utilising magnetic reference layers which can be used in both reflectivity and small angle neutron scattering experiments.The effort will not only allow us to develop new research routes, it will also serve as an excellent platform for developing the national expertise on neutron scattering, instrumentation as well as forming bridges for utilising existing and future infrastructures.