Emma Hall

Project: Computational identification of catalytic covalent organic frameworks

What inspired you to pursue this project and join the DAMC CDT?

Throughout my academic journey, I have been driven by a fascination with the interdisciplinary nature of chemistry and a desire to solve real-world problems. When thinking of the next steps during my MChem degree at the University of Liverpool, I knew I wanted to stay in the field but was searching for the right path.

This direction became clear during my Master’s project, where I first discovered the world of computational chemistry. In collaboration with experimentalists at the University of St Andrews, I explored the electronic properties of novel perovskite materials using Density Functional Theory (DFT). This experience opened my eyes to how technology can accelerate discovery by bridging the gap between theoretical results and experimental data.

I was inspired to join the DAMC CDT because it perfectly integrates traditional chemistry with the evolving digital world. My project allows me to build upon my computational foundations while developing skills in high-throughput screening, AI, and automation – vital for the future of science. The CDT’s collaborative environment also offers a unique opportunity to work alongside peers in other related disciplines. Not only am I thrilled to continue my research at a globally recognised institution for automated chemistry, but I also get to continue living in a city I love.

What is your research project about, and what impact do you hope it will have?

My research focuses on Covalent Organic Frameworks (COFs) – a class of crystalline, porous polymers that offer a unique platform for heterogeneous catalysis. COFs are thought to mimic the high selectivity of existing catalysts like zeolites through confinement of reactions within their pores; however, these confinement effects remain relatively unexplored due to the complexities of experimental synthesis and characterisation. I am using computational modelling to understand the structure-property relationships influencing catalytic activity of COFs. By applying Density Functional Theory (DFT), Molecular Dynamics (MD), and Machine Learning (ML), I aim to resolve structural ambiguities and assess how specific pore geometries impact substrate diffusion and selectivity. This work is fundamental for establishing a high-throughput screening workflow to identify the most promising catalytic COF candidates for experimental testing. Ultimately, this project will bridge computational and experimental work, accelerating the discovery of materials that would otherwise be impossible to identify through experimental trial and error alone.

What has been the most exciting or rewarding part of your PhD journey so far and how does your project benefit from being part of an interdisciplinary CDT?

The most rewarding part of my PhD so far has been the opportunity to travel for specialised training. Being part of the DAMC CDT provides the funding and connections to learn high-level computational skills from experts across the country, which directly helps my research. This support has been a huge benefit, allowing me to build a much broader technical skill set and access resources I wouldn't have otherwise.