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）是一类新的高级材料，由熵稳定的氧化物中的5个或更多元素组成。金属离子在原本晶格上的随机分布赋予它们独特的特性，并且在高温下它们仅稳定。它们在理论上和现实世界中都具有一系列重要的特性，包括高离子电导率，以及高介电常数，有趣的机械性能和低热导率，可接近基本的无定形极限。该项目的目的是使用计算建模来了解基本层面的HEO物理学，并利用该知识为重要的未来应用设计材料。材料的结构和新颖性的复杂性使它们成为理论家和计算科学家的丰富研究主题。但是，HEO的组成和配置空间是巨大的，这种复杂性需要对原子间相互作用进行复杂的建模。一类新的机器学习间原子势（MLIP）非常适合此目的。因此，该提案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，以及如何使用不同形式的组成，结构和缺陷障碍的效果通过MLIP的参数化来设计和预测新材料。第二个是采用这些原子结构，并在高熵氧化物中的热传输物理学建模，从而跨越了晶体和无定形材料之间的机制。通过实验与理论之间的合作，新材料将在原子上设计用于指导工业应用和制造。