权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

金属有机框架 (MOF) CSD 子集拓扑表征的新知识和工具

基本信息

批准号：
2483281
负责人：
金额：
--
依托单位：
University of Sheffield
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2483281
关键词：
New Knowledge Tools Topological Characterisation

项目摘要

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.

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，非常适合于揭示关键金属-有机骨架(MOF)性质的趋势，建立结构-性质关系，并指导未来的合成努力。在过去的三年里，对CSD MOF子集结构的计算分析主要集中在几何结构(如最大孔径、孔体积、比表面积)和气体吸附性能的表征上。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性质，如有机/无机团簇的局部环境和CSD MOF子集中已确定的MOF的基本拓扑。这一博士学位的重点是CCDC科学家在结晶学、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技术诀窍。这一目标包括开发工具来表征MOF并预测结构-功能关系，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CCDC和谢菲尔德大学的广泛同行研究人员，以及获得材料科学、化学和大数据科学之间的接口技能，这些技能在工业界和学术界都很受欢迎。