Modelling disorder in magnesium battery cathode materials

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Atomistic modelling of materials offers great potential for predicting desirable properties before material synthesis in the laboratory. For example, we can probe hypothetical materials to find those with an optimum band gap for solar cell applications, or those that are able to efficiently transport metal ions for use as a battery cathode material. During my PhD we will model energy materials using quantum chemical methods including plane-wave Density Functional Theory (DFT). In this method a bulk material can be modelled with a careful selection of the unit cell, which is then repeated in 3-dimensional space with periodic boundary conditions. Usually the real crystal is approximated as static and pristine (without any defects), which misses much of the rich physics and behaviour associated with thermal vibrations and atomic-scale imperfections. In this project, we will use lattice dynamics and defect physics to consider processes beyond this idealised model. We will use ab-initio packages running on Oswald (the Northumbria supercomputer) and Archer2 (the national supercomputer) to generate DFT data. The output will be analysed and post-processed using open source codes. We will also develop our own custom open-source codes for analysing data according to the needs of the project. The PhD will begin by modelling chalcogenide perovskite materials, an emerging class of materials that are being developed for use in optoelectronic devices. These materials are proposed to be a good substitute for the popular albeit toxic lead-based perovskites. In this project we will model the zirconium based chalcogenide perovskite, BaZrS3, working in collaboration with experimental colleagues who are synthesising the compound using ball-milling. DFT modelling will determine the structure and thermal properties of the material. A further analysis of the vibrational modes will produce the IR and Raman characteristics of the phonon modes to compare with the experimental results. A detailed analysis of the reactants, intermediates and the by-products produced during the synthesis of the perovskite will introduce more insight into the reaction mechanism and the relative stability of each of the compounds. With the skills and experience acquired during the lattice dynamics project we will move onto modelling cathodes for multivalent batteries. As the inherent limitations of monovalent, lithium-ion based batteries are being realised, and as lithium reserves are becoming depleted, new candidates with a higher theoretical energy density are being proposed. We will the study magnesium based spinel compounds. Usually pristine structures of crystals are modelled using DFT, whilst any chemical disorder or defects are completely neglected. However the process of charging and discharging leads to unavoidable disorder in battery materials, which in turn determines the energetics of defect formation and material stability. With current DFT methods, the fully charged and discharged cathodes can be readily modelled, whilst relatively little is known about the intermediate processes that take place during the charge cycle. We will use a cluster expansion techniques to model disorder and predict material performance through the battery charge cycle. This project will explore various computational techniques, such as designing automated workflows, high-throughput DFT evaluation, development of post-processing codes and machine learning. We will navigate through different crystal systems and establish structure-property relationships, predicting the bulk, defect and transport properties of emerging energy materials. These insights, developed throughout the course of my PhD, will bring the scientific research community one step closer to meeting the challenges of Net Zero.