Issue 42, 2020

Artificial neural networks for the prediction of solvation energies based on experimental and computational data

Abstract

The knowledge of thermodynamic properties for novel electrolyte formulations is of fundamental interest for industrial applications as well as academic research. Herewith, we present an artificial neural networks (ANN) approach for the prediction of solvation energies and entropies for distinct ion pairs in various protic and aprotic solvents. The considered feed-forward ANN is trained either by experimental data or computational results from conceptual density functional theory calculations. The proposed concept of mapping computed values to experimental data lowers the amount of time-consuming and costly experiments and helps to overcome certain limitations. Our findings reveal high correlation coefficients between predicted and experimental values which demonstrate the validity of our approach.

Graphical abstract: Artificial neural networks for the prediction of solvation energies based on experimental and computational data

Supplementary files

Article information

Article type
Paper
Submitted
10 juil. 2020
Accepted
02 oct. 2020
First published
12 oct. 2020

Phys. Chem. Chem. Phys., 2020,22, 24359-24364

Artificial neural networks for the prediction of solvation energies based on experimental and computational data

J. Yang, M. J. Knape, O. Burkert, V. Mazzini, A. Jung, V. S. J. Craig, R. A. Miranda-Quintana, E. Bluhmki and J. Smiatek, Phys. Chem. Chem. Phys., 2020, 22, 24359 DOI: 10.1039/D0CP03701J

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