New Knowledge and Tools for Topological Characterisation of the CSD Subset of Metal-Organic Frameworks (MOFs)

计算高通量筛选（HTS）具有在短时间内研究数千种结构的无与伦比的潜力，非常适合揭示关键金属有机骨架（MOF）性质的趋势，建立结构-性质关系并指导未来的合成工作。在过去的三年中，CSD MOF子集结构的计算分析主要集中在几何特征（如最大孔径、孔体积、比表面积）和气体吸附特性上。显然，以前的计算工作已经提供了重要的见解，但它还没有完全表征诸如有机/无机簇的局部环境以及CSD MOF子集中已识别的MOF的底层拓扑结构等特性。该博士学位专注于CCDC科学家在晶体学，材料科学和软件开发方面的专业知识和学科的协同结合，以及谢菲尔德团队在数据库生成，多孔材料表征和分子水平模拟方面的专业知识。这一目标包括开发表征mof和预测结构-功能关系的工具，以加速材料的发现。这是一个多学科项目，成功的候选人将受益于剑桥大学和谢菲尔德大学的CCDC广泛的同行研究小组，并获得在工业界和学术界都有很高需求的材料科学，化学和大数据科学之间的界面技能。

With unparalleled potential to investigate thousands of structures in a short time, computational high-throughput screening (HTS) is extremely well-suited to unravel trends in key metal-organic framework's (MOF) properties, establish structure-property relationships and guide future synthetic efforts. In the last three years, the computational analysis of structures in the CSD MOF subset has been focused on the characterization of geometric (e.g. largest pore size, pore volume, surface area) and gas adsorption properties. Clearly, previous computational work has delivered important insights, but it has not yet fully characterised properties such as the local environment of organic/inorganic clusters and the underlying topology of the identified MOFs in the CSD MOF subset. this PhD studentship focuses on synergetic combination of expertise and disciplines from the CCDC scientists in crystallography, materials science and software development, and the Sheffield team's know-how in database generation, porous materials characterisation, and molecular-level simulations. Included in this goal is the development of tools to characterise MOFs and predict structure-function relationships to accelerate materials discovery.This is a multidisciplinary project and the successful candidate will benefit from an extensive peer-group of researchers at the CCDC in Cambridge and the University of Sheffield, as well as acquiring skills at the interface between, materials science, chemistry and big data science, that are in high demand in both industry and academia.