New Knowledge and Tools for Topological Characterisation of the CSD Subset of Metal-Organic Frameworks (MOFs)
Lead Research Organisation:
University of Sheffield
Department Name: Chemical & Biological Engineering
Abstract
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.
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.
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/R513313/1 | 30/09/2018 | 29/09/2023 | |||
2483281 | Studentship | EP/R513313/1 | 30/09/2020 | 27/03/2024 | Lawson Glasby |
EP/T517835/1 | 30/09/2020 | 29/09/2025 | |||
2483281 | Studentship | EP/T517835/1 | 30/09/2020 | 27/03/2024 | Lawson Glasby |