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Machine Learning (Neural Networks) in Materials Science

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L2, Mathematical Institute

University of Oxford, Radcliffe Observatory Quarter, Woodstock Rd

Oxford

OX2 6GG

United Kingdom

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Sir Harry Bhadeshia (FRS, FREng, FNAE) is the Tata Steel Professor of Metallurgy at the University of Cambridge, and leads the Phase Transformations & Complex Properties group (http://www.msm.cam.ac.uk/phase-trans/).

If you travel from the UK to France via the channel tunnel, your carriage is riding on rails made of a particular kind of steel that Prof. Bhadeshia invented. He has also developed the world's first bulk nanostructured steel (metallic structure controlled at nanoscale) in commercial production, and was used to create the strongest armour to date. His main research interest is in phase transformation, both the theory and mathematical modelling. He led a joint EPSRC effort with the National Physical Laboratory to build the Materials Algorithms Project (MAP) which serves as a centre for the "validation" and distribution of algorithms of use in the modelling of materials.

In this talk he will gives us an insight into machine learning, in particular neural networks in dealing with extremely complex problems in materials science.

Spaces are limited. Please RSVP and have your ticket ready at the door.

This talk is co-organised by the Oxford Artificial Intelligence Society (OxAI) and the Oxford 3D Printing Society (Ox3DP)
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Location

L2, Mathematical Institute

University of Oxford, Radcliffe Observatory Quarter, Woodstock Rd

Oxford

OX2 6GG

United Kingdom

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