About the Project
Research into the rational design of metal-organic framework (MOF) materials has grown significantly over the last 20 years, with these materials finding applications in areas such as gas storage and separation, catalysis as well as the healthcare domain (Nat. Chem. 2016, 8, 990–991). Due to their exceptional structural and chemical tuneability, thousands of MOFs have been already synthesised. Last year, our paper “The Development of a CSD subset: A Collection of MOFs for Past, Present and Future” presented the world’s first publicly available and automatically updated database of ca. 84,000 MOF structures. A major question that often arises with the application of MOFs and other functional materials is how to find the most promising structures for a given application in the diverse pool of existing materials. Due to practical constraints, experimental trial-and-error discovery of materials is not fast enough, and therefore more efficient alternatives must be developed to accelerate the discovery and deployment of new adsorbent materials.
The focus of this PhD project is to develop and employ high-throughput computational techniques through grand canonical Monte Carlo (GCMC), molecular dynamics and DFT calculations to explore different MOF applications, from energy storage to the separation of valuable chemical feedstocks, as well as developing conductive materials for electronics industry. Included in this goal is the systematic development of machine learning and data mining tools to study and predict structure-function relationships to accelerate materials discovery in combination with experimental efforts.
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