Antiferromagnetic materials for spintronics

传统的微电子元件，如晶体管和固态存储器，是通过操纵电流来处理和存储信息的。正是这些设备中的电子流产生了热量，导致了它们的能源效率低下。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耗的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成为一种颠覆性的低功耗技术。然而，早期原型设备中使用的材料尚未充分优化以用于实际应用，并且可能的材料组合的大参数空间使得这成为一个极具挑战性的问题。该项目旨在采用预测第一性原理材料建模来筛选反铁磁（AF）自旋电子学（特别是mn基合金）的有前途的材料，根据它们的体晶体性能，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]。预测性理论建模在提前确定有问题的缺陷并提出缓解缺陷的途径（例如，通过修改材料成分）方面具有不可估量的价值。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.