Antiferromagnetic materials for spintronics

传统的微电子组件，如晶体管和固态存储器，通过操纵电流来处理和存储信息。正是这些设备中的电子流动产生了热量，并对它们的低能效负责。利用电子的量子力学自旋特性(所谓的自旋电子学)的新技术为快速和低功率信息处理和存储提供了前景。特别是，基于反铁磁材料的自旋电子器件最近作为一种破坏性的低功率技术出现了]。然而，早期原型器件中使用的材料还没有充分优化到实际应用中，而可能的材料组合的大参数空间使这一问题变得极其具有挑战性。本项目旨在利用预测第一性原理材料模型来筛选反铁磁(AF)自旋电子学(特别是锰基合金)的有前途的材料，对照它们的大块晶体性质，以及薄膜器件中存在的异质界面、本征点和可能影响材料性能的扩展缺陷[3]。预测性理论模型在预先识别有问题的缺陷并提出缓解途径(例如，通过修改材料成分)方面可以发挥非常重要的作用。这些发现将用于指导实验材料的生长和表征。

Conventional microelectronic components such as transistors and solid-state memories process and store information by the manipulation of electrical currents. It is the flow of electrons in these devices that generates heat and is responsible for their poor energy efficiency. New technologies that harness the quantum mechanical spin property of electrons (so called spintronics) offer the prospect of rapid and low power information processing and storage. In particular spintronic devices based on antiferromagnetic materials have very recently emerged as a disruptive low power technology]. However, materials employed in early prototype devices are not yet optimised sufficiently for practical application and the large parameter space of possible material combinations makes this an extremely challenging problem.This project aims to employ predictive first principles materials modelling to screen promising materials for antiferromagnetic (AF) spintronics (specifically Mn-based alloys) against their bulk crystal properties, together with heterointerfaces present in thin-film devices, intrinsic point and extended defects which may impact on material properties [3]. Predictive theoretical modelling can be invaluable in identifying problematic defects in advance and suggesting routes to their mitigation (e.g., through modification of material composition). The findings will be used to guide experimental materials growth and characterisation.