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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Synthetic pre-training on ML-generated tensor data followed by fine-tuning on ground-truth calculations improves data efficiency for graph models of solid-state NMR parameters when the pre-training and fine-tuning domains match.
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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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Synthetic pre-training of graph-network models for predicting solid-state NMR parameters
Synthetic pre-training on ML-generated tensor data followed by fine-tuning on ground-truth calculations improves data efficiency for graph models of solid-state NMR parameters when the pre-training and fine-tuning domains match.