Metal-organic frameworks (MOFs) are porous, crystalline materials consisting of metal atoms or clusters connected by organic molecules. They are being widely investigated for CO2 capture and storage, as well as greener alternatives to distillation processes for separating gas mixtures. However, their scale-up production and industrial applications are lagging behind. To a significant extent we attribute this profound gap between the promise of MOFs and their industrial use to the fact that, while the search for functions and properties of MOFs shifted towards computational screening, their synthesis largely remains the traditional trial-end-error endeavour.
The Sarkisov group develops fundamental understanding of these processes on a molecular level, accelerating their deployment through integration of this knowledge in process design and material development.
We are currently researching methods to bridge the gap between MOF discovery and manufacture computationally, aggregating and interpreting pre-existing experimental synthesis methods to find hidden trends connecting synthesis conditions and material performance. This project will use natural language processing and machine learning techniques to mine and identify the best precursors and conditions for improved yield and quality of existing materials, as well as identifying routes to hypothetical new materials.
The successful applicant will join the vibrant Multiscale Modelling group of 13 academics at the University of Manchester, with a broad set of research interests, expertise and topics under investigation (https://bit.ly/2ImQpDz). Further, the project will collaboration with Dr David Fairen-Jimenez (Cambridge) and Prof. Sotirios Tsaftaris (Edinburgh), providing opportunity for exposure to advanced experimental techniques and AI methods, respectively.
Applicants should have or expect to achieve a 1st class MEng or MSc with Distinction in Engineering, Chemistry, Physics or Mathematics
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.
We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).
All appointments are made on merit.
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