Path Integral Quantum Spin Dynamics

磁性材料对技术应用尤为重要。在过去的50年里，它们一直被用作计算机数据存储的主要来源，促进了计算和互联网的大规模扩张。自旋电子学等较新的领域希望利用磁学和电子学来制造结合了这两个领域的最好的新设备，例如现在全球所有主要铸造厂都在制造用作随机存取存储器的磁性隧道结。模拟大脑工作方式的新型计算已经被提出，磁性材料看起来在其中扮演着重要的角色。随着量子计算和超导计算的快速发展，科学家们正在考虑如何将磁性材料用作设备。我们必须了解磁铁的稳定性，这一点至关重要。信息通常是通过将磁体置于两种相反状态之一来编码的。磁铁有可能改变状态，我们就会丢失信息。设备必须经过精心设计，这样才不太可能发生这种情况。主要的问题来自热，但量子力学也会引起诸如隧道效应之类的效应，即磁体可以自发地改变状态。当器件越小，温度越低，以及某些类型的磁体，如反铁磁体，量子效应就变得更加显著。虽然一般原理已被理解，但我们目前还没有工具来模拟特定材料和器件的这些效应，以了解它们可能如何影响磁性材料的新应用。本研究项目旨在建立一个新的工具来模拟磁性材料中的量子效应，并将其用于研究反铁磁体，这是未来存储器件的有力候选者。我们将使用理查德·费曼创建的一种名为路径积分的数学技术，它允许用更大的一组经典系统来模拟量子系统。这使我们能够以相对较低的计算成本在非常大的范围内包括量子效应。这种模型在分子动力学模型中已被证明是非常成功的，但还没有被应用到自旋动力学中来模拟磁体。除了开发基本方法外，我们还将创建一个软件包，以便世界各地的研究人员可以在他们的努力中使用该方法来预测材料性质和解释实验。该项目将使磁性材料的定量建模超越目前的可能，并推动未来的研究。

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.