| EPSRC Reference: |
EP/S014985/1 |
| Title: |
Enhancing Machine Learning with Physical Constraints to Predict Microstructure Evolution |
| Principal Investigator: |
Clarke, Professor N |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Physics and Astronomy |
| Organisation: |
University of Sheffield |
| Scheme: |
Standard Research - NR1 |
| Starts: |
01 December 2018 |
Ends: |
30 November 2020 |
Value (£): |
250,600
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Complex fluids & soft solids |
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| EPSRC Industrial Sector Classifications: |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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05 Jun 2018
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ASD - FS Interview Panel
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Announced
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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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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.shef.ac.uk |