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Computational photochemistry of materials and photocatalytic water splitting and CO2 reduction

Using a combination of density functional theory and time-dependent density functional theory, we study the optical properties and photochemistry of materials such as conjugated polymers, self-assembled materials and nanoparticles. A special area of interest is understanding the application of such materials as photocatalysts for water splitting and CO2 reduction. Recently we have also started to look into how machine learning methods can help with accelerating such calculations for large libraries of materials. Much of this work is performed in collaboration with the experimental group of Andy Cooper in Liverpool, as well as the groups of Profs. Durrant and Nelson at Imperial College.

Computational electrochemistry of materials

Using density functional theory, we study the electrochemical properties of systems, such as polymers and hydro- and organogels, in order to understand their applications as photoconductors, optorheological materials, as well as battery electrodes and electrolytes. Much of this work is performed in collaboration with the experimental groups of Dave Adams and Emily Draper in Glasgow and Alex Cowan in Liverpool.

Benchmarking methods

Accurately predicting the optical and electronic properties of molecules and materials is a challenging task for quantum chemical methods. We study the accuracy of approaches such as Time-Dependent Density Functional Theory by comparing its predictions to both experiment and inherently more accurate but computationally more expensive wave function (e.g. IP/EA-EOM-CCSD) or many-body perturbation theory (e.g. GW) calculations. Much of this work is done in collaboration with Karol Kowalski and the  NWChem group at the Pacific Northwest National Laboratory. 

Chemical space exploration, property space mapping, global optimisation and machine learning

The chemical space of materials is typically enormous. For example, in the case of conjugated polymers one could construct up to 250,000 ordered binary co-polymers from a modest pool of 500 monomers. As a group we are interested in exploring this chemical space and that of its properties, the property space through a combination of brute-force high-throughput virtual screening and global optimisation, accelerated, where possible, by the use of semi-empirical methods and machine learning, as computationally efficient alternative to density functional theory. Our focus in this area is currently predominantly on organic materials but previously we applied similar methods to inorganic bulk materials and nanostructures.


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