Hybrid Machine Learning for Enhanced Prediction of Diffusion Coefficients in Liquids

TL;DR

This paper presents a hybrid machine learning model that combines the Stokes-Einstein equation with ML to accurately predict diffusion coefficients in liquids, ensuring physical consistency and broad applicability.

Contribution

The novel ESE model integrates physics-based equations with ML, providing accurate, physically consistent diffusion predictions using only molecular structure data.

Findings

ESE outperforms previous models like SEGWE in accuracy.

The model requires only SMILES strings as input.

ESE is accessible via an interactive web interface.

Abstract

Diffusion coefficients are key thermophysical properties for modeling mass transport in liquids, but experimental data are scarce, making reliable prediction methods indispensable. In the present work, we introduce a new method for predicting diffusion coefficients of molecular components at infinite dilution in pure liquid solvents by integrating the Stokes-Einstein (SE) equation with machine learning (ML). Unlike previous ML approaches, the resulting hybrid Enhanced Stokes-Einstein (ESE) model provides strictly physically consistent predictions for diffusion coefficients as a function of temperature across a broad range of binary mixtures. Trained and validated using an extensive compilation of literature data for infinite-dilution diffusion coefficients in binary liquid systems, ESE achieves significantly higher prediction accuracies than the previous state-of-the-art model, SEGWE, while requiring only the SMILES strings encoding of the molecular formulae of the components of interest as additional inputs, which are always available. This simplicity makes ESE broadly applicable, e.g., for process design and optimization. The ESE model and its source code are fully disclosed and are directly accessible via an interactive web interface at https://ml-prop.mv.rptu.de/.