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Prediction of diffusion coefficients in mixtures with tensor completion.

Zeno Romero1, Kerstin Münnemann1, Hans Hasse1

  • 1Laboratory of Engineering Thermodynamics, RPTU Kaiserslautern-Landau, Erwin-Schrödinger-Str. 44, 67663 Kaiserslautern, Germany. fabian.jirasek@rptu.de.

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Summary
This summary is machine-generated.

A new hybrid tensor completion method (TCM) accurately predicts temperature-dependent diffusion coefficients in binary mixtures. Combining machine learning with active learning and experimental data significantly improves predictions for transport properties.

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Area of Science:

  • Physical Chemistry
  • Chemical Engineering
  • Computational Chemistry

Background:

  • Predicting diffusion coefficients in mixtures is vital but challenging due to scarce experimental data.
  • Existing machine learning (ML) models, like matrix completion methods (MCMs), are limited to single-temperature predictions.
  • Accurate MCM predictions require extensive, high-quality experimental data for each specific temperature.

Purpose of the Study:

  • To develop a hybrid tensor completion method (TCM) for predicting temperature-dependent diffusion coefficients at infinite dilution in binary mixtures.
  • To overcome the limitations of single-temperature predictions in existing ML models.
  • To enhance the accuracy and applicability of predictive models for transport properties.

Main Methods:

  • A hybrid tensor completion method (TCM) using Tucker decomposition was developed.
  • The TCM was trained on experimental diffusion coefficient data at multiple temperatures (298 K, 313 K, 333 K) using a Bayesian framework.
  • Active learning (AL) strategies guided the acquisition of new experimental data via pulsed-field gradient (PFG) NMR measurements.

Main Results:

  • The developed TCM demonstrated significantly improved prediction accuracy for diffusion coefficients across a wide temperature range (268 K–378 K) compared to established models.
  • Incorporating newly measured diffusion data, obtained through AL-guided experiments, further enhanced the TCM's predictive performance.
  • The hybrid TCM successfully extrapolated predictions to temperatures beyond the initial training data.

Conclusions:

  • The hybrid tensor completion method (TCM) offers a powerful approach for predicting temperature-dependent diffusion coefficients in binary mixtures.
  • Combining data-efficient ML techniques with adaptive experimentation (AL) is a promising strategy for advancing predictive modeling of transport properties.
  • This work highlights the potential for improved accuracy and broader applicability of ML models in chemical thermodynamics and transport phenomena.