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Machine Learning in Materials Science

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  • UserProfessor Sir Harry Bhadeshia, Department of Materials Science & Metallurgy, University of Cambridge
  • ClockWednesday 31 January 2018, 14:00-15:00
  • HouseGC13.

If you have a question about this talk, please contact Andrew Morris.

Tea/coffee & cake will be available in the C-block foyer from 1.30pm

Long before machine learning and artificial intelligence became throwaway terms and on occasions seen as threats to our very existence, there were useful attempts to take advantage of what are essentially mathematical methods to deal with complex problems in science.

In the context of materials science, the first paper seems to have been published in 1991 [1]. My own interest was stimulated in 1992, when I did not understand a presentation made at a conference, and followed this up on returning to Cambridge, where I discovered David MacKay (a leader in information theory), then our Physics Department. We started working together and published the first paper in 1995 [2]. The method turned out to be so powerful that we published 14 papers together in unfunded research over a period of 24 years.

In this lecture, I will deal explicitly with the following:
 (a) A simple and transparent explanation of the method.
 (b) Question whether there is any intelligence involved.
 (c) Show how the technique has led to remarkable PREDICTIONS that have been subsequently been verified experimentally.
 (d) Describe the best way of disseminating the outcomes.

[1] Ghaboussi, J., J. H. Garrett Jr, and Xiping Wu. “Knowledge-based modeling of material behavior with neural networks.” Journal of Engineering Mechanics 117.1 (1991): 132-153.
 [2] Bhadeshia, H. K. D. H., D. J. C. MacKay, and L-E. Svensson. “Impact toughness of C–Mn steel arc welds–Bayesian neural network analysis.” Materials Science and Technology 11.10 (1995): 1046-1051.

Biography: Harry Bhadeshia is the Tata Steel Professor of Metallurgy at the University of Cambridge, with primary interest in the science that leads to novel alloys of iron.

This talk is part of the School of Metallurgy and Materials Colloquia series.

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