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The hybrid Enhanced Stokes-Einstein model is extended to concentration-dependent self-diffusion coefficients and multicomponent solvents with HADES, which significantly outperforms benchmark prediction methods.
Forget complex simulations: this new model predicts liquid diffusion coefficients with state-of-the-art accuracy using only SMILES strings as input.
By combining tensor completion with active learning, this method achieves markedly improved predictions of temperature-dependent diffusion coefficients in mixtures, even with limited experimental data.