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Laboratory of Engineering Thermodynamics, RPTU Kaiserslautern, Erwin-Schrödinger-Str. 44, 67663 Kaiserslautern, Germany
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A new hybrid model predicts electrolyte behaviors for 9,296 systems, overcoming the limitations of traditional models that require extensive experimental data.
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.
