HEOM: High entropy oxides: understanding their unique properties and dynamics using machine learning interatomic potentials
Lead Research Organisation:
Queen Mary University of London
Department Name: Physics
Abstract
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.
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.
