Principal Component Analysis of Quantum Materials Data: a Study in Augmented Intelligence

Add to your list(s) Download to your calendar using vCal

• Jorge Quintanilla, University of Kent
• Thursday 25 February 2021, 13:00-14:00
• https://bham-ac-uk.zoom.us/j/8268225040.

If you have a question about this talk, please contact Dr Hannah Price.

There is much interest currently in the potential of machine learning to tease useful information out of complex data on materials. Here I ask whether this can work when only experimentally accessible data, i.e. averages rather than microstates, are available. I will use Principal Component Analysis to study simulated neutron-scattering data on cluster quantum magnets [1] and experimental muon-spin relaxation curves from various superconducting and magnetic materials [2]. While the algorithms can perform certain functions, such as detection of phase transitions, automatically, I will argue that their best use is in providing human scientists with new ways to look at the data – an approach that has come to be known as “augmented”, rather than “artificial”, intelligence.

References:

[1] https://arxiv.org/abs/2011.08234. [2] https://arxiv.org/abs/2010.04742.

This talk is part of the Theoretical Physics Seminars series.

This talk is included in these lists:

• Bham Talks
• Theoretical Physics Seminars
• What's on in Physics?
• https://bham-ac-uk.zoom.us/j/8268225040

Note that ex-directory lists are not shown.