Computational optimisation of photoactive dye pairs for designing novel, highly efficient dye-sensitised solar cells.

The overarching goal of this project is to further the power conversion efficiency of dye-sensitized solar cells (DSCs) - a renewable energy technology that can be effectively integrated into windows and wearables. One tactic to improve output current from a DSC device is by using multiple materials that absorb light in complementary regions of the solar spectrum. In particular, the aims of this project are to optimise key light-harvesting materials and combinations thereof when used synergistically in the same device. Using a large, custom-made database of such light-harvesting materials, optimum partner materials for well known, highly performing ones have been identified. Electronic structure techniques are being used to model the identified material pairs in their working environment, yielding information that will then be used to predict device parameters purely by computational means. This is the first time, to our knowledge, that this predictive method will be applied to multiple light-harvesting materials within the same device. By demonstrating that key parameters, such as power conversion efficiency, can be accurately predicted without relying on experimental data for input, automating the design of complementary materials is enabled via computational screening. The following work will then involve designing a workflow that can predict improved, hitherto unseen DSC materials, potentially using machine learning to tackle the optimisation. This effort is motivated by the increasingly urgent need for a diverse set of efficient renewable energy technologies. With an estimated 40% of energy consumption occurring in an urban environment, integrating efficient DSCs in windows of urban buildings would maximise harvested energy from light falling on surfaces that would otherwise go unused.

Computing, data and communications infrastructure have transformed modern life. They all require software, security and trained personnel to work effectively and it has become common to use the term e-Infrastructure to refer to the whole of this interconnected ecosystem. E-Infrastructure has become a major contributor to advances in science and technology and it is clear that no industry will be able to compete internationally unless it exploits e-Infrastructure at highest level. The proposed EPSRC Centre for Doctoral Training in Computational Methods for Materials Science is focused on the development of new functionality in existing software, and even entirely new codes that will address challenges in materials that cannot presently be addressed. The UK is making large capital investments in e-Infrastructure but these investments will only achieve a fraction of their potential impact unless investments are also made in software development. The CDT will primarily focus on software development for materials science, which is itself, of course, extremely broad. However, the training provided in numerical methods, modern software development techniques and the exposure to present and emerging computational hardware means that the students will have the skilled set to work in any area of software after their PhDs. Given the universally acknowledge lack of highly trained personnel in software development one of our most important impacts will be in providing nearly 80 people who can apply this training in industry, including the very large number of UK software-based SMEs, or academia. The emphasis of the training and subsequent research project in the CDT is on development of innovative new methods for materials modelling and these will have impact in both academia and industry in further expanding the capability of materials simulation and the range of phenomena and processes that can be simulated and/or the amount of information that can be extracted from experiments. Thus we expect the CDT to have a significant impact across a very broad spectrum of disciplines.