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Prediction of diffusion coefficients in mixtures with tensor completion
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DOI：10.1039/D6CP00732E.png)
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Abstract
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Predicting diffusion coefficients in mixtures is crucial for many applications; as experimental data remain scarce; and machine learning (ML) offers promising alternatives to established semi-empirical models. Among ML models; matrix completion methods (MCMs) have proven effective in predicting thermophysical properties; including diffusion coefficients in binary mixtures. However; MCMs are restricted to single-temperature predictions; and their accuracy depends strongly on the availability of high-quality experimental data for each temperature of interest. In this work; we address this challenge by presenting a hybrid tensor completion method (TCM) for predicting temperature-dependent diffusion coefficients at infinite dilution in binary mixtures. The TCM employs a Tucker decomposition and is jointly trained on experimental data for diffusion coefficients at infinite dilution in binary systems at 298 K; 313 K; and 333 K. Predictions from the semi-empirical SEGWE model serve as prior knowledge within a Bayesian training framework. The TCM then extrapolates linearly to any temperature between 268 K and 378 K; achieving markedly improved prediction accuracy compared to established models across all studied temperatures. To further enhance predictive performance; the experimental database was expanded using active learning (AL) strategies for targeted acquisition of new diffusion data by pulsed-field gradient (PFG) NMR measurements. Diffusion coefficients at infinite dilution in 19 solute + solvent systems were measured at 298 K; 313 K; and 333 K. Incorporating these results yields a substantial improvement in the TCM's predictive accuracy. These findings highlight the potential of combining data-efficient ML methods with adaptive experimentation to advance predictive modeling of transport properties.
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