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Details of Grant 

EPSRC Reference: EP/S014985/1
Title: Enhancing Machine Learning with Physical Constraints to Predict Microstructure Evolution
Principal Investigator: Clarke, Professor N
Other Investigators:
Cabral, Dr J Wilkinson, Dr R D
Researcher Co-Investigators:
Project Partners:
Procter & Gamble Solvay Group (UK)
Department: Physics and Astronomy
Organisation: University of Sheffield
Scheme: Standard Research - NR1
Starts: 01 October 2018 Ends: 31 March 2020 Value (£): 250,600
EPSRC Research Topic Classifications:
Artificial Intelligence Complex fluids & soft solids
EPSRC Industrial Sector Classifications:
Chemicals
Related Grants:
Panel History:
Panel DatePanel NameOutcome
05 Jun 2018 ASD - FS Interview Panel Announced
Summary on Grant Application Form
De-mixing is one of the most ubiquitous examples of self-assembly, occurring frequently in complex fluids and living systems. It has enabled the development of multi-phase polymer alloys and composites for use in sophisticated applications including structural aerospace components, flexible solar cells and filtration membranes. In each case, superior functionality is derived from the microstructure, the prediction of which has failed to maintain pace with synthetic and formulation advances. The interplay of non-equilibrium statistical physics, diffusion and rheology causes multiple processes with overlapping time and length scales, which has stalled the discovery of an overarching theoretical framework. Consequently, we continue to rely heavily on trial and error in the search for new materials.

Our aim is to introduce a powerful new approach to modelling non-equilibrium soft matter, combining the observation based empiricism of machine learning with the fundamental based conceptualism of physics. We will develop new methods in machine learning by addressing the broader challenge of incorporating prior knowledge of physical systems into probabilistic learning rules, transforming our capacity to control and tailor microstructure through the use of predictive tools. Our goal is to create empirical learning engines, constrained by the laws of physics, that will be trained using microscopy, tomography and scattering data. In this feasibility study, we will focus on proof-of-concept, exploring the temperature / composition parameter space for a model blend, building the foundations for our ambition of using physics informed machine learning to automate and accelerate experimental materials discovery for next generation applications.

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Organisation Website: http://www.shef.ac.uk