Machine learning and quantum theory of magnets for energy efficient and renewable energy technologies

该项目与正在与HetSys合作伙伴Forschungszentum Julich合作进行的研究密切相关。该项目还将加强刚刚开始的与美国波士顿东北大学材料科学家在开发磁功能材料的新材料加工方面的合作。学生将有机会参观这两个合作机构。磁性材料在技术上是必不可少的--用于电机、发电机、固态冷却、电子设备、数据存储、医疗、玩具等。尽管磁性的影响很容易在宏观层面上理解，但它源于电子胶水的复杂集体行为，同时将材料的原子核结合在一起并产生磁矩。在这个项目中，我们将通过使用机器学习工具来识别原子论的经典自旋模型，这些模型来自于电子基本量子力学的计算数据。从他们的研究中，我们将发现设计新磁体的方法，减少稀土金属等关键元素的含量。这项工作将直接与国际合作伙伴的理论工作和实验测量有关。随着更节能技术、可再生能源供应和设备进一步小型化的推动，对更强大和更便宜的磁性材料的需求非常迫切。该项目将是正在进行的计算模型开发的一部分，以了解固有磁性，完善设计原则，并帮助寻找新的功能磁体。磁性材料由原子核的晶格组成，周围环绕着相互作用的电子的粘合剂。此外，支撑材料磁性的电子也负责决定其原子的排列。这种电子流体的复杂性对理论和计算模型提出了根本挑战--它可能导致的磁性来自于许多电子的合作行为导致的围绕原子位的复合自旋。这种经典自旋对之间以及它们之间存在相互作用。原则上，这些多自旋参数可以通过计算电子的基本量子力学来确定。到目前为止，我们已经发展了描述原子位周围自旋平均有序的量，即局域磁序参数，来描述系统自由能的团簇展开，我们可以精确地描述许多磁性以及它们如何随着温度、组成和外场的变化而变化。然而，从这样的从头计算数据中提取和研究精确的经典自旋哈密顿量是一项具有挑战性的任务，也是本项目的核心。为了使这项工作更上一层楼，并使其能够描述用于设计关键元素水平降低的新磁体以及具有有趣的拓扑磁性结构(Skyrmions)的材料的多组分磁性材料，我们需要开发机器学习工具来确定自由能的形式，而不是我们目前的特别方法。这项工作还将加强我们对多组分合金中原子的排列如何受到应变和磁场的影响的建模，从而对合作者正在开发的新材料加工产生影响。

The project is closely connected to ongoing research being conducted in collaboration with HetSys partner Forschungszentrum Julich. The project will also enhance the collaboration which has just begun with materials scientists at Northeastern University in Boston in the USA on developing novel materials processing for magneto-functional materials. There will be opportunities for the student to visit both partner institutes.Magnetic materials are technologically indispensable - used in motors, generators, solid state cooling, electronic devices, data storage, medical treatment, toys etc. Although the effects of magnetism are easily understood on the macroscopic scale, it has its origins in the complex collective behaviour of the electronic glue, simultaneously binding the nuclei of the material together and generating magnetic moments. In this project we will identify atomistic, classical spin models by using machine learning tools on data from calculations of the fundamental quantum mechanics of the electrons. From their study we will discover ways to design new magnets with reduced amounts of critical elements such as rare earth metals. The work will relate directly to theoretical work and experimental measurements by International Partners.With the drive towards more energy efficient technologies, renewable energy supplies and further miniaturisation of devices, there is an urgent demand for stronger and cheaper magnetic materials. This project will be part of ongoing development of computational modelling to understand intrinsic magnetic properties, to refine design principles and to aid the search for new functional magnets. A magnetic material comprises a crystalline lattice of nuclei surrounded by a glue of septillions of interacting electrons. Moreover, the same electrons which underpin the magnetism of a material are also responsible for determining the arrangements of its atoms. The complexity of this electron fluid presents a fundamental challenge for theory and computational modelling - the magnetism it can lead to comes from composite spins coalescing around atomic sites as a result of the cooperative behaviour of many electrons. There are interactions between pairs of such classical spins and among clusters of them. In principle these multi-spin parameters can be determined from calculations of the fundamental quantum mechanics of the electrons. To date we have developed a cluster expansion of the free energy of the system in terms of the quantities which describe the average order of the spins around the atomic sites, i.e. local magnetic order parameters, and we can describe accurately many magnetic properties and how they vary with temperature, composition and applied fields.The extraction and investigation of an accurate model classical spin-Hamiltonian, however, from such ab initio data is a challenging task and it is at the heart of this project. To take the work to the next level and enable it to describe multicomponent magnetic materials for the design of new magnets with reduced levels of critical elements as well as materials with intriguing topological magnetic structures (skyrmions) we need to develop machine learning tools to determine the form of the free energy rather than our current ad hoc approach.This work will also enhance our modelling of how arrangements of atoms in multi-component alloys can be affected by the application of strain and magnetic fields and hence have an impact on novel materials processing being developed by collaborators.