Design of Organic Electronic Materials using Predictive Modelling

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My overall project aim is to replace the metals used in smart devices with responsive self-assembled organic molecules. The use of metal in our everyday technologies is problematic. The acquisition, mining and disposal of these rapidly depleting metals have many issues financially and environmentally. The use of organic materials as a possible alternative to metals is a solution, as they are more abundant, less expensive and use processing methods that are less energy intensive. Recently there is an ever-expanding list of examples of organic based materials being successfully used for devices such as LEDs and being used as photocatalysts, that are outperforming their metal competitors. This shows that organics can be a real option. However, a problem with organic-based materials is that there is so much possibility not only in the molecular structure, but also in the assembly of the molecules to produce different aggregates, post-assembly processing and then drying into thin films to eventually prepare a device. These high-performing organics are often found serendipitously or after years of research. With so many different iterations of the same molecule let alone different molecules, it can be overwhelming knowing where to start to look. This is where we can make a real difference. To overcome this ambiguity in where to start testing new molecules, I will develop a prediction model where researchers will be able to narrow down both which molecules to prepare based on the desired functionality, and also the aggregation and processing required of the molecule. I will develop this through quantitative structure-property relationship (QSPR) based prediction models by using data collected through a high-throughput approach. This will enable researchers to quickly collect data on different molecules, assembly methods and conditions, post-assembly additives and alignment. I will start by focusing on mechanoresponsive devices. These devices are prepared using materials that can sense movement by changing their resistivity upon being bent. They are used in devices such as tocodynamometers on labour wards and in smart prosthetics for amputees. My lab currently has materials that show promise in this area, but I want to understand what makes these molecules work whilst others do not. This will be achieved by exploring how molecular structure and the self-assembly of these materials affect the supramolecular structure, and then how this morphology influences properties such as conductivity and flexibility. Simply observing a relationship between chemical structure and morphology to material properties will be really useful, but I aim to go a step further by talking all this information and using it to build predictive models. The models will then be tested by exploring chemical space and informing us which molecules and which self-assembly methods are most likely to give us materials with the desired properties and which will not. The suggested materials will then be synthesised and tested, and again the information fed back into these models, continuing improving them.The prediction models will be invaluable to the field of organic electronics, as being able to predict what molecules to make for their properties has the potential to expand the field drastically. Currently, materials are often made and then applied to an application afterwards, whereas here I will start with the application first and tailor the design and fabrication without wasting time and resources on material discovery. This will allow more time to be used on testing and further development to make them competitive with metal-based alternatives.