Modelling disorder in magnesium battery cathode materials

材料的原子模型为在实验室合成材料之前预测所需的性能提供了巨大的潜力。例如，我们可以探测假设的材料，以找到那些具有太阳能电池应用的最佳带隙的材料，或者那些能够有效地传输金属离子用作电池阴极材料的材料。 在我的博士学位期间，我们将使用量子化学方法（包括平面波密度泛函理论（DFT））来建模能量材料。在这种方法中，可以通过仔细选择单位单元来模拟散装材料，然后在具有周期性边界条件的三维空间中重复。通常，真实的晶体被近似为静态的和原始的（没有任何缺陷），这错过了与热振动和原子尺度缺陷相关的许多丰富的物理和行为。在这个项目中，我们将使用晶格动力学和缺陷物理来考虑超越这个理想模型的过程。我们将使用在Oswald（Northumbria超级计算机）和Archer 2（国家超级计算机）上运行的从头算程序包来生成DFT数据。将使用开放源码对产出进行分析和后处理。我们还将根据项目的需要开发自己的定制开源代码，用于分析数据。 博士将开始通过建模硫属钙钛矿材料，正在开发用于光电器件的新兴材料类。这些材料被认为是流行的铅基钙钛矿的良好替代品，尽管有毒。在这个项目中，我们将模拟锆基硫属钙钛矿BaZrS 3，与使用球磨合成化合物的实验同事合作。DFT建模将确定材料的结构和热性能。进一步分析振动模式将得到声子模的红外和拉曼特征，与实验结果进行比较。对钙钛矿合成过程中产生的反应物、中间体和副产物的详细分析将使人们更深入地了解反应机理和每种化合物的相对稳定性。 凭借在晶格动力学项目中获得的技能和经验，我们将转向多价电池的阴极建模。由于单价锂离子电池的固有局限性正在被认识到，并且随着锂储量逐渐耗尽，正在提出具有更高理论能量密度的新候选电池。我们将对镁基尖晶石化合物进行研究。通常，晶体的原始结构是使用DFT建模的，而任何化学无序或缺陷都被完全忽略。然而，充放电过程导致电池材料中不可避免的无序，这反过来又决定了缺陷形成和材料稳定性的能量学。使用当前的DFT方法，完全充电和放电的阴极可以容易地建模，而对充电周期期间发生的中间过程知之甚少。我们将使用团簇扩展技术来模拟无序状态，并通过电池充电周期预测材料性能。 该项目将探索各种计算技术，例如设计自动化工作流程，高通量DFT评估，后处理代码和机器学习的开发。我们将通过不同的晶体系统导航，并建立结构-性能关系，预测新兴能源材料的体积，缺陷和传输特性。这些见解，在我的博士学位过程中发展，将使科学研究界更接近迎接净零的挑战。

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.