Combining Chemical Robotics and Statistical Methods to Discover Complex Functional Products

Robotics and statistical machine learning have revolutionised manufacturing of mechanical devices and our ability to deal with large amounts of data in many areas of human activities. Chemical manufacturing remains one area where both robotics and statistics have seen very limited uptake. However, both are the likely solutions to many challenges facing the chemicals manufacturing industries. The challenge of sustainable manufacturing requires a rapid switch to locally available renewable feedstocks, new sources of energy and use of rapidly re-configurable intensive reactor technologies. However, conventional methods of process development are slow: this stems from the inherent complexity of chemical processes, including multiple interactions of many components over a very broad range of length and timescales, from behaviour of single molecules to the behaviour of cubic-meter-scale manufacturing reactors. This is where machine learning algorithms could provide the solution, with the ability to rapidly identify the underlying interactions and to design the most useful experiments to perform. To use such algorithms effectively we require a new type of a chemical experiment - highly automated and 'intelligent', equipped with sensors and the ability to link with mathematics, the data handling and the machine learning. The main advance on knowledge that this proposal will realise, is the translation of discovery of new chemical products to their manufacture. For this in this project the chemical robot will learn to identify key process parameters that will impact on scaling the process and will help to develop a scale-up model of a process. This ability will have a tremendous impact in all sectors of chemical products manufacturing, with the main societal impact of better process safety, guaranteed product quality and reduced impact of manufacturing on climate change through reduced emissions and feedstocks waste.

Automation of the processes of discovery and of development of manufacturing protocols is a breakthrough technology, which will revolutionize high-value manufacturing in the chemical industries. Robotics is already used in several chemical sectors, specifically in agrochemical and consumer products industries, for high-throughput development of formulations; catalysts development companies are using robots for combinatorial high-throughput material synthesis. However, the link of robotics with machine learning and its use to guide molecular and product discovery is yet at a nascent state. The project will solve the identified key fundamental challenges in developing the future industrial platforms based on robotics and AI. The subsequent enhancement of the UK industrial base in high-value manufacturing will contribute to a range of important societal impacts, including: more cost-effective development of medicines, faster access to complex and customised functional products, delocalization of manufacturing, leading to demand for skilled work-force away from large cities, and many others, yet difficult to predict positive societal outcomes. The project will achieve further impact through its contribution to the people-pipeline through training and mentoring of a group of multi-disciplinary scientists with strong core skills in chemistry, chemical engineering and statistics.

Impact deliverables: In summary, to achieve impact we will deliver
- Three open-innovation workshops, facilitating interactions with academic community and potential end-users.
- Interactions with centres of excellence and networks: DaM, CPI and EPSRC funded CMAC, targeting self-selected communities of early adopters.
- Papers, presentations in industrially accessible outlets, and open source research software, promoting rapid uptake of methods and tools.
- Exemplars to demonstrate the methodology and its advantages over current state-of-the-art, providing numerical justification for industrial adoption of robotics and AI in chemical industries.
- A website, social media platforms including videos show casing the various aspects e.g. discovery, DoE, scale up, promoting SET subjects in the wider society.
- Industrial, as well as academic, PDRA secondments and identification of exploitation and commercialisation routes.
- Public engagement via science and engineering events.