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Machine Learning&(SI)STM

Machine Learning&(SI)STM. Peter Wahl School of Physics and Astronomy University of St Andrews. Current Research. Research: Unconventional Superconductivity Iron Pnictides Noncentrosymmetric Superconductors Heavy Fermion Materials Imaging of magnetic order in quantum materials

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Machine Learning&(SI)STM

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  1. Machine Learning&(SI)STM Peter Wahl School of Physics and Astronomy University of St Andrews

  2. Current Research • Research: • Unconventional Superconductivity • Iron Pnictides • Noncentrosymmetric Superconductors • Heavy Fermion Materials • Imaging of magnetic order in quantum materials • Atomic-scale imaging of strain-stabilized phases of matter • Confirmation of topological superconductivity in Sr2RuO4

  3. Machine Learning Machine Spectroscopic STM data

  4. Cluster Analysis for Spectroscopic Maps Cluster Analysis 1 Spectroscopic STM data 2

  5. Normal state electronic inhomogeneity near EF in FeSe0.4Te0.6 T=16K

  6. Electronic inhomogeneity? after subtraction of parabolic background Correlation coefficient 0.64 Higher than would be expected!

  7. Use of AI for STM • AI algorithms for data analysis • Pattern recognition (e.g. defects, characteristic spectra, (quasi-periodic patterns)) • Artifical intelligence for operation of STM • tip preparation & judgement of tip quality • Optimization of feedback parameters

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