Evolutionary property prediction for molecular materials

In the simplest of definitions, chemistry concerns the synthesis and the properties of molecules. Supramolecular chemistry is known as "chemistry beyond the molecule", where groups of molecules assemble without forming chemical bonds. Supramolecular systems have exciting applications as sensors, molecular switches, molecular machines (such as molecules that "walk" along a track) and as catalysts that speed up other reactions. We would like to design such systems for new applications by deducing the properties of a supramolecular system from a simple chemical sketch or idea - much as an architect's sketch of a building, for example, can reliably predict its function. However, when we simply draw a molecule, we do not know what properties it will have, nor how it will assemble. Worse, in many cases we cannot be confident that the particular molecule can in fact be synthesised at all since the assembly rules in chemistry are, still, much less well developed than those in architecture. Instead, synthetic chemists use their chemical intuition to guide them as to the best experiments to try. Then, if successful in getting a product, they must characterise the material and its properties. Even in state-of-the-art labs, this is a slow process - a new molecule can take a year to prepare, let alone to characterise. Sometimes even small changes in the reaction can have a large effect on the outcomes, hence 'intuitive' design breaks down, particularly as systems become more complex.

In this proposal, our aim is to provide the same computational 'blueprint' for supramolecular materials in order to allow synthetic research teams to discover new, targeted functions in a much more rapid timeframe. We will develop computer software that will allow us to predict the best molecule for a particular type of device. We aim to use our software for more efficient "sieves" that can separate molecules be size, shape or chemistry, for more efficient molecules for optoelectronic devices such as solar cells and more efficient catalysts for the petrochemical and pharmaceutical industry. The software is based on evolutionary algorithms, these are approaches that are inspired by Darwin's theory of evolution and pit candidate materials against each other as with the "survival of the fittest" in nature. Each generation of candidates is tested with simple calculations that predict their properties as a measure of their fitness. The fittest candidates are most likely to survive to the next generation, but also random mutations of their features will occur and pairs of candidates will parent new offspring with mixtures of their features - just as occurs in nature. These evolutionary approaches are extremely effective ways of exploring very complex problems where there are many variables that influence outcome. The development of this procedure specifically for molecular materials is exciting because it will allow us to direct chemists towards the best synthetic systems and our overarching goal is to show that computational modelling can be responsible for the discovery of new materials with useful new applications, rather than simply rationalising results from synthetic teams. Ultimately we hope this will allow the computational design of new materials to become reliable enough such that it is a routine precursor to synthesis in the laboratory, just as an architect's sketch is the first step to constructing a building.

The beneficiaries of this research are academia, petrochemical, pharmaceutical and optoelectronics industry and the general UK population. These benefits are envisaged on a multi-decade timescale. Molecular materials are used, or have the potential to be used, in a wide range of technological applications of importance to the UK economy, including petrochemical and pharmaceutical separations and catalysis, optoelectronics, nanomedicine, sensors and magnetic devices. Our research will also provide insight into the key structural factors affecting the performance of molecular materials. To optimise current devices, for instance improve the energy conversion efficiency and cost of organic solar cells, or to discover new applications, methods to effectively design and thus guide synthetic efforts are needed. There is potential for the predictions to save many person-years and the expense of wasted syntheses of materials that will not have optimal performance. UK-based exploitation of the ensuing patents or devices from our research would ultimately benefit the UK economy. We will provide academic and industrial parties with the opportunity to find optimal molecular materials for a wide range of applications. Through improving efficiency of any of the materials in the multiple listed applications, we have the opportunity to create cheaper devices that are less wasteful of resources in their manufacture. For example, the generation of more efficient organic solar cells can make these devices more economically viable for the wider community, including in developing countries, spreading their use over fossil fuels. This is just one of the potential routes to environmental benefits stemming from the research.

For academics the software we will develop will be freely available and it will be simple to include additional modules for new functionalities to be optimised. Computational modelling is known to add value to the UK economy- for instance between a 3:1 and 9:1 return from R&D investment and an overall contribution of 1% of UK GDP (Goldbeck Consulting Ltd. Report, 2012: http://www.psi-k.org/reports/Economic_impact_of_modelling.pdf). The PDRA hired will be further trained in software development, in molecular materials and in transferable skills, all of which will benefit any future employer. Students at Imperial will be exposed to the approach and the software developed either through lectures, computational lab courses or research projects in the group.