Path Integral Quantum Spin Dynamics

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Magnetic materials are particularly important for technological applications. They have been used for the last 50 years as the main source of computer data storage, facilitating the massive expansion in computing and the internet. Newer fields such as spintronics hope to use the magnetism alongside electronics to make new devices which combine the best of both worlds, for example magnetic tunnel junctions which are now being manufactured by all of the major global foundries for use as random access memories. New types of computing which mimic how the brain works have been suggested and magnetic materials look to play an important role in these. With quantum computing and superconducting computing developing at a rapid pace, scientists are considering how magnetic materials will be used devices. It's vitally important that we understand how stable magnets are. Information is normally encoded by putting magnets into one of two opposite states. There is a probability that the magnet can change state and we lose information. Devices have to be carefully designed so that this is very unlikely. The main issue comes from heat, but quantum mechanics can also cause effects such as 'tunnelling', where the magnet can spontaneously change state. Quantum effects become more significant when devices are smaller, temperatures are lower, and in some types of magnets such as antiferromagnets. While the general principle is understood, we don't currently have the tools to simulate these effects for specific materials and devices to understand how they might impact the new applications for magnetic materials.This research project is to build a new tool for modelling quantum effects in magnetic materials and to use it to study antiferromagnets which are a strong candidate for use in future memory devices. We will use a mathematical technique created by Richard Feynman called path integrals which allow quantum systems to be modelled by a larger set of classical systems. This allows us to include quantum effects on a very large scale with relatively low computational costs. This sort of modelling has proved very successful in molecular dynamics modelling but has not yet been applied to spin dynamics for modelling magnets. As well as developing the fundamental method we will create a software package so that researchers around the world can use the method in their efforts to predict material properties and interpret experiments. The project will enable quantitative modelling of magnetic materials beyond what is currently possible and drive future research.