ABOUT US

The School of Science and Technology at Nottingham Trent University (NTU) is an exciting multidisciplinary environment for learning, teaching and research, with some of the best facilities in the UK.
We pride ourselves on delivering high-quality teaching and diverse, real-world research. We specialise in biosciences, chemistry, computing and technology, as well as engineering, forensic science, mathematics, physics and sport science. This mix of traditional and modern subjects encourages and inspires future innovators.
In the Department of Chemistry, courses are taught in modern, innovative spaces, offering excellent career prospects and accreditation by The Royal Society of Chemistry. The Department has an active research community with a diverse range of knowledge and expertise.
For any informal queries about the role, please contact Dr Matt Addicoat at matthew.addicoat@ntu.ac.uk.

Molecular Framework Materials (MFMs) are of strong interest worldwide due to their porosity, crystallinity and chemical functionalizability / tunability making them appealing for a broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis.
A major drawback when applying computational chemistry to MFMs is that, even for “rigid” MFMs, especially when functionalized, their structures are disordered due to the random orientation of linkers. Such structural disorder and flexibility are well-known in crystallography, but less in computational chemistry, where a distinct structure is required to undertake a calculation. This problem has largely been ignored, and researchers have chosen a single “representative” MFM
structure, ignoring positional disorder which makes the calculation feasible but at the cost of ignoring the local structure. This is problematic, as the local structure has strong effects on guest binding, diffusion and even the dynamic behaviour of the entire MFM.
In this project, funded by the Leverhulme Trust, we will develop a machine learning platform to describe the the effects of isomerization on adsorption and diffusion in porous Molecular Framework Materials (e.g. MOFs, COFs, ZIFs, MOPs…).
The project extends from our initial work on developing desciptors for isomerization in MOFs: https://doi.org/10.1039/D3QI01065A

What we are looking for:

• A PhD in Chemistry, Materials Science, Physics or a related field
• Experience in python (ASE, scikit-learn, pytorch) is essential
• Experience in uncertainty quantification or statistics applied to quantum chemistry and machine learning would be advantageous

For more details, please take a look at the role profile. We’ll still consider applications even if you don’t meet every single one of the requirements, so don’t be put off if you don’t match them perfectly.
Interviews; w/c 14th July