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

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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.

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