Defects by Design; Understanding and Controlling Defect Processes in Advanced Energy Materials

迫切需要能够提供必要的成本降低和性能提升以创造可持续的绿色经济的下一代能源材料。利用高通量计算材料建模策略，我们的目标是通过控制晶体缺陷的实现来增强相关能源材料（即光伏太阳能电池、电池和热电）的性能。当人体在DNA中产生数十亿个遗传密码时，不可避免地存在称为基因突变的缺陷。同样，在每种固态材料中也存在类似的不可避免的缺陷，这些缺陷可以显著提高或降低器件性能，例如太阳能电池效率，电池容量和电池寿命。然而，识别提高性能或触发降级和效率降低的特定缺陷具有挑战性。现代缺陷理论为理解和控制晶体中的缺陷（预测浓度和输运速率）以及解释缺陷过程的实验探测（包括扩散的激活能和电子/振动/自旋特征）提供了强有力的工具。 在这个项目中，我们将开发一种新的软件基础设施，用于表征晶体固体中的缺陷，并预测建模和实验之间直接比较的签名。具体而言，混合密度泛函理论（DFT）的计算方法将通过计算缺陷形成能、表征缺陷核心能级和识别稳定氧化态来研究相关能源材料的缺陷化学。从这项工作中获得的见解将应用于固态能源材料研究和开发的前沿，通过提供克服当前限制的设计策略来优化能源设备的性能和成本。预测将与剑桥、帝国理工学院伦敦和伦敦大学学院的实验伙伴一起验证。此外，这项研究将提供一个新的框架，在固态材料中的缺陷，这将证明是一个有价值的研究社区工具，适用于其他材料系统的缺陷设计的深入高通量调查。该项目与EPSRC的“能源”研究主题最相关，特别是“能源应用材料”，“储能”（两者都被列为EPSRC投资增长的目标领域）的研究领域，以及“物理科学”主题中的“凝聚态物质：电子结构”领域。

There is an urgent demand for next-generation energy materials which can provide the necessary cost decreases and performance boosts to create a sustainable green economy. Using a high-throughput computational materials modelling strategy, we aim to enhance the performance of relevant energy materials, namely photovoltaic solar cells, batteries and thermoelectric, via the controlled implementation of crystalline defects.When the body creates billions of genetic code in DNA, there are inevitable imperfections termed genetic mutations. Likewise, there are similar inevitable imperfections in every solid-state material which can significantly enhance or degrade device performance, such as solar cell efficiency, battery capacity and battery lifetime. However, it is challenging to identify the specific defects which improve performance or trigger degradation and efficiency reduction. Modern defect theory provides a powerful tool for understanding and controlling defects in crystals (predicting concentrations and transport rates) and in interpreting experimental probes of defect processes, including activation energies for diffusion, and electronic/vibrational/spin signatures. In this project, we will develop a novel software infrastructure for the characterisation of defects in crystalline solids and predicting signatures for direct comparison between modelling and experiment. Specifically, the computational approach of hybrid Density Functional Theory (DFT) will be implemented to investigate the defect chemistry of relevant energy materials, through the calculation of defect formation energies, characterisation of defect core levels and identification of stable oxidation states. The insights gained from this work will be applied to the cutting edge of solid-state energy material research and development, optimising energy device performance and cost by providing design strategies to overcome current limitations. Predictions will be validated with experimental partners at Cambridge, Imperial College London and UCL. Moreover, this research will provide a novel framework for in-depth high-throughput investigations of defects in solid-state materials, which will prove a valuable research community tool for defect design applicable to other materials systems. This project is most relevant to the EPSRC research theme of 'Energy', specifically the research areas of 'Materials for Energy Applications', 'Energy Storage' (both of which are classified as target areas for growth in EPSRC investment), in addition to the area 'Condensed Matter: Electronic Structure' in the 'Physical Sciences' theme.