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

HEOM：高熵氧化物：使用机器学习原子间势了解其独特的性质和动力学

• 批准号: EP/X034429/1
• 负责人: Oliver Dicks
• 金额: $ 33.41万
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX034429%2F1
• 关键词: HEOM; entropy; oxides; understanding; their

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.

高熵氧化物 (HEO) 是一类新型先进材料，由 5 种或更多元素组成的熵稳定氧化物组成。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。它们在理论上和实际应用中都具有一系列重要的特性，包括高离子电导率、高介电常数、有趣的机械特性和接近基本非晶态极限的低热导率。该项目的目标是使用计算模型从根本上了解 HEO 的物理原理，并利用这些知识为未来的重要应用设计材料。材料结构的复杂性和新颖性使它们成为丰富的研究课题对于理论家和计算科学家来说。然而，HEO 的组成和构型空间巨大，这种复杂性需要对原子间相互作用进行复杂的建模。一类新的机器学习原子间势（MLIP）特别适合此目的。因此，该提案的主要目标是双重的。首先是开发计算和理论技术，以了解 HEO 热力学稳定性的基本原理，以及如何通过 MLIP 的参数化，利用不同形式的成分、结构和基于缺陷的无序的影响来设计和预测新材料。第二个是采用这些原子结构并对跨越晶体和非晶材料之间的高熵氧化物中的热传输物理进行建模。通过实验和理论之间的合作，新材料将以原子方式设计，以指导工业应用和制造。