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

用于节能和可再生能源技术的机器学习和磁体量子理论

• 批准号: 2729474
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2729474
• 关键词: Machine; learning; quantum; theory; magnets

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.

该项目与HetSys合作伙伴Forschungszentrum Julich合作进行的正在进行的研究密切相关。该项目还将加强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刚刚开始的合作。学生将有机会参观这两个合作机构。磁性材料在技术上是不可或缺的-用于电动机，发电机，固态冷却，电子设备，数据存储，医疗，玩具等。虽然磁性的影响在宏观尺度上很容易理解，它起源于电子胶的复杂集体行为，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的经典自旋模型，这些工具来自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铁的方法，减少稀土金属等关键元素的含量。这项工作将与国际合作伙伴的理论工作和实验测量直接相关。随着能源效率更高的技术、可再生能源供应和设备的进一步智能化，对更强大、更便宜的磁性材料的需求迫切。该项目将是正在进行的计算建模开发的一部分，以了解固有的磁性，完善设计原则，并帮助寻找新的功能磁体。磁性材料包括由数十亿个相互作用的电子组成的胶所包围的原子核的晶格。此外，支撑材料磁性的相同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建模提出了一个根本性的挑战-它可能导致的磁性来自于由于许多电子的合作行为而在原子位置周围聚结的复合自旋。在这些经典自旋对之间以及它们的簇之间存在相互作用。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，我们已经发展了一个描述原子位置周围自旋的平均序的量，即局部磁序参数的系统自由能的簇展开，并且我们可以精确地描述许多磁性质以及它们如何随温度、成分和外加场而变化。从这种从头开始的数据是一项具有挑战性的任务，它是这个项目的核心。将工作提升到一个新的水平，使其能够描述多组分磁性材料，用于设计具有降低的关键元素水平的新磁体以及具有有趣的拓扑磁性结构的材料（skyrmions）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设方法。这项工作也将增强我们对多原子中原子排列的建模，成分合金可以受到应变和磁场的影响，因此对合作者正在开发的新材料加工产生影响。