A Gaussian mixture model is used to learn spectral densities from 2DES experiments, enabling extraction of vibronic couplings, spectral extrapolation, and optimized experiment selection across simulated and experimental systems.
and Coker, David F
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Derives a memory kernel identity via Nakajima-Zwanzig operators to quantify coherence's modulation of energy transfer rates, applied to a dimer-phonon bath model.
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Streamlining Analysis and Design of Two-Dimensional Electronic Spectroscopy using Machine Learning
A Gaussian mixture model is used to learn spectral densities from 2DES experiments, enabling extraction of vibronic couplings, spectral extrapolation, and optimized experiment selection across simulated and experimental systems.
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Characterizing the functional role of quantum coherence in energy transfer
Derives a memory kernel identity via Nakajima-Zwanzig operators to quantify coherence's modulation of energy transfer rates, applied to a dimer-phonon bath model.