| Project Detail |
A rapidly-growing number of industrial, technological and healthcare processes are built around using the energy of absorbed light to drive chemical reactions or energy transfer; for example, photocatalysts use absorbed light to perform chemical reactions which might be otherwise impossible, conjugated polymers are being incorporated into new lightweight photovoltaic devices to convert light into energy, and several photosensitizing drugs are now approved for photodynamic therapies to treat cancers.
To design the next-generation of photoactive molecules with targeted properties, it is essential that we are able to understand and rationalise the mechanism of light-induced chemical reactions; this is where computer simulations can be transformative. Unfortunately, modelling photochemical dynamics is one of the most difficult frontier challenges of computational chemistry; studying the coupled motions of all of the electrons and nuclei in a molecule after it absorbs light requires highly-specialized computer simulation methods (in particular, quantum wavefunction propagation on multiple electronic states), and so has remained the domain of highly-specialized experts.
Our recent work has begun to transform the landscape of this field by combining machine-learning strategies with accurate wavefunction propagation methods; our emerging "on the fly" quantum dynamics strategy now enables us to perform simulations of photochemical dynamics in a matter of hours to days, whereas the established methodology which has prevailed during the last two decades (e.g. grid-based wavefunction propagation on global potential energy surfaces) typically requires months of simulation- and user-time.
The "big idea" of this proposal is to take these new emerging quantum simulation methods and transform them into a true "black box" tool which can be used, by experts and non-experts alike, to perform accurate simulations of photochemical dynamics. This will require an initial period of software and methodology development to improve the usability and efficiency of our exisiting approach. To further increase the scope of our on-the-fly simulation methods, we will then develop strategies which can explicitly account for the influence of solvent molecules on photochemical dynamics; after all, most interesting photo-driven processes take place in solution or solid-phases.
These method developments will then open the gateway to an enormous range of new applications of photochemical dynamics simulations. In this proposal, we identify two state-of-the-art applications which we will be able to address once our "black box" quantum dynamics methodology has been established. First, we will design new universal fluorophore tags which exhibit environment-dependent fluorescence spectra; these tags might find application in bioimaging applications, provide new insights into cellular environments, or in detecting contaminants in water pipes. Second, we will design new photo-acid catalysts for activating olefins, molecules which constitute a large fraction of crude oil but which generally have low commerical value; we will investigate a new photocatalysis method which might be able to transform these chemicals into much higher value commodity chemicals. In a unique twist, both of these applications of quantum simulations will run in tandem with experimental synthesis and spectroscopic characterisation. This will provide a route to validating (and improving!) our simulation methods, and will also function as a feedback loop to enable true computer-driven rational design of photoactive molecules.
Overall, this proposal will seamlessly integrate computational method development and high-impact applications in cutting-edge photochemical sensing and catalysis. Bringing together experts in simulation, spectroscopy and synthesis, our research team is uniquely positioned to deliver on these promising new research directions. |
