Rapid computational screening of materials for energy storage

这个博士项目将使用材料信息学-计算机科学和材料科学的结合-开发软件，用于快速筛选和识别用于储能的新化学空间。该项目将专注于替代由无毒或稀有元素组成的现有组件，例如高密度锂离子电池中的LiCoO2阴极。其中至关重要的是与材料可持续性相关的指标，包括元素丰度、生产率以及地缘政治因素。该研究将结合联合收割机的第一原理模拟（密度泛函理论）的元素，结合通过统计分析（机器学习）开发的化学过滤器。

This PhD project will use materials informatics - a combination of computer science and materials science - to develop software for the rapid screening and identification of new chemical spaces for energy storage. The project will be focused on the substitution of existing components that are formed of non-toxic or rare elements, e.g. LiCoO2 cathodes in high-density Li-ion batteries. Critical to this will be metrics relating to materials sustainability, including element abundance, production rates, as well as geopolitical factors. The research will combine elements of first-principles simulations (density functional theory), combined with chemical filters developed through statistical analysis (machine learning).