Artificial neural networks for the prediction of solvation energies based on experimental and computational data
文献信息
Jiyoung Yang, Matthias J. Knape, Oliver Burkert, Virginia Mazzini, Alexander Jung, Vincent S. J. Craig, Ramón Alain Miranda-Quintana, Erich Bluhmki
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.
期刊推荐

Biocatalysis and Biotransformation

Journal of the Indian Institute of Science

Medicinal Chemistry Research

Journal of Asian Natural Products Research

Acta Metallurgica Sinica-English Letters

NDT & E International

Herald of the Russian Academy of Sciences

Atomization and Sprays

Bioorganic & Medicinal Chemistry Letters

Chinese Journal of Chemistry
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来源期刊
Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.


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