NISMA uses PCA and Gaussian mixture clustering on hyperspectral near-field infrared images to map four distinct structural and electronic phases in La-doped SrSnO3, and attributes their mobility differences to strain-modulated effective mass.
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Machine Learning-Assisted Nano-imaging and Spectroscopy of Phase Coexistence in a Wide-Bandgap Semiconductor
NISMA uses PCA and Gaussian mixture clustering on hyperspectral near-field infrared images to map four distinct structural and electronic phases in La-doped SrSnO3, and attributes their mobility differences to strain-modulated effective mass.