HEOM: High entropy oxides: understanding their unique properties and dynamics using machine learning interatomic potentials

高熵氧化物（High entropy oxides，HEO）是一种由5种或5种以上元素组成的熵稳定氧化物。金属离子在晶格上的随机分布赋予它们独特的性质，并且它们仅在高温下稳定。它们在理论上和在真实的世界应用中具有一系列重要的性质，包括高离子电导率、高介电常数、有趣的机械性质和接近基本无定形极限的低热导率。该项目的目标是使用计算建模来理解HEO的基本物理学，并使用这些知识来设计未来重要应用的材料。结构的复杂性和材料的新奇使其成为理论家和计算科学家的丰富研究课题。然而，HEO的组成和构型空间是巨大的，这种复杂性需要对原子间相互作用进行复杂的建模。一种新的机器学习原子间势（MLIP）是唯一适合于这一目的。因此，这个建议的主要目的是双重的。首先是开发计算和理论技术，以了解HEO中热力学稳定性的基本原理，以及如何通过MLIP的参数化来设计和预测不同形式的成分，结构和基于缺陷的无序的影响。第二个是采取这些原子结构和模型的高熵氧化物的热输运的物理跨越结晶和非晶材料之间的制度。通过实验和理论之间的合作，新材料将被原子化设计，以指导工业应用和制造。

High entropy oxides (HEOs) are a new class of advanced materials composed of 5 or more elements in an entropy stabilized oxide. The random distribution of metal ions on an otherwise crystal lattice give them unique properties, and they are only stable at high temperatures. They have a range of important properties both theoretically and in real world applications, including high ionic conductivities, coupled with high dielectric constants, interesting mechanical properties and low thermal conductivities that approach the fundamental amorphous limit. The goal of this project will be to use computational modelling to understand the physics of HEOs at the fundamental level and use that knowledge to design materials for important future applications.The complexity of the structures and novelty of the materials makes them a rich topic of research for theorists and computational scientists. However, the compositional and configurational space of HEOs is gigantic and this complexity requires sophisticated modelling of interatomic interactions. A new class of machine learning interatomic potentials (MLIPs) are uniquely suited for this purpose.The main aims of this proposal are therefore twofold. The first is to develop computational and theoretical techniques to understand the fundamental principles of thermodynamic stability in HEOs and how the effects of different forms of compositional, structural and defect-based disorder can be used to design and predict new materials through the parameterization of MLIPs. The second is to take these atomistic structures and model the physics of thermal transport in high entropy oxides which straddle the regimes between crystalline and amorphous materials. Through collaboration between experiment and theory new materials will be atomistically designed to guide industrial applications and manufacturing.