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Details of Grant 

EPSRC Reference: EP/V003321/1
Title: Poly(ML): Machine Learning for Improved Sustainable Polymers
Principal Investigator: Siviour, Professor CR
Other Investigators:
Clifton, Professor DA Williams, Professor CK
Researcher Co-Investigators:
Project Partners:
Biome Technologies Lewtas Science and Technologies Materiom
Department: Engineering Science
Organisation: University of Oxford
Scheme: Standard Research - NR1
Starts: 01 May 2020 Ends: 30 April 2022 Value (£): 506,089
EPSRC Research Topic Classifications:
Manufacturing Machine & Plant Materials Synthesis & Growth
EPSRC Industrial Sector Classifications:
Chemicals Manufacturing
Related Grants:
Panel History:  
Summary on Grant Application Form
The environmental impacts of polymer waste are well-publicised, and there is an increasing demand from policymakers and the public to reduce polymer use and improve recycling or degradability. In addition, there is an opportunity to reduce environmental impact by producing the polymers from bio-derived inputs, which are often themselves waste products from other processes. Whilst the polymer industry is fully engaged with these topics, there is still significant basic research to be done: to produce bio-derived polymers with excellent engineering properties, and to understand the different opportunities

presented by the very wide range of possible input materials.

It is also well known that in many fields, machine learning has played a vital and important role in making better use of very large data sets resulting from complex physical processes; examples include use of data in healthcare and weather prediction. Importantly, machine learning enables us to build on our current best understanding of the subject whilst efficiently exploring new possibilities, for example in synthesis or processing of polymers, in an efficient and well-guided manner. This will potentially allow us to make advances in polymer design much more quickly than can currently be done

through processes of trial and error.

Unfortunately, the application of machine learning to polymers is challenging because of their very complex structures and behaviours. In this project, we will bring together experts in polymer synthesis, polymer engineering and machine learning in order to solve this challenge. Specifically, we will be able to produce polymer systems in which we can carefully vary and measure the relevant properties, such as stiffness or strength, in order to produce high quality training data from which the machines can learn. We will then use the machine learning to show us how to optimise the polymer properties.

During this project, we will produce open-source computational tools which will be made available on the internet. Industrial polymer producers will be able to use these toolboxes to develop new polymers, and will also be able to expand

and adapt them for future needs. The toolboxes will also support 'distributed manufacturing', allowing small-scale manufacturers worldwide to obtain locally produced polymers designed to have properties that meet their needs.

Combined with new production methods such as 3D printing, this will help to deliver a low-waste, localised 'circulareconomy',meeting specific local manufacturing needs. Hence, this project will play a key role in global polymer development over a very wide range of economic scales.

Key Findings
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Potential use in non-academic contexts
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Summary
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Further Information:  
Organisation Website: http://www.ox.ac.uk