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Using Machine Learning to Predict Enthalpy of Solvation

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Abstract

Enthalpy of solvation is an important thermodynamic property for studying molecular interactions. However, measuring enthalpies of solvation is non-trivial and time-consuming. Therefore, models that can predict enthalpy of solvation values are of significant worth to the general community. Here we present such models, based upon the Acree enthalpy of solvation open data dataset, which can be used to predict enthalpy of solvation values directly from structure. We created machine learning models for enthalpies of solvation in ethanol using open Chemistry Development Kit descriptors that have overall test-set R2 values of 0.89–0.90 and test-set root mean squared error values of 6.60–7.10 kJ·mol−1. The accuracy of the models was improved by limiting them to a single dominant cluster. Since our models were developed under Open Notebook Science conditions, they are fully reproducible and our techniques transparent and easily adaptable to other solvents.

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Acknowledgements

The authors acknowledge the influence of Jean-Claude Bradley and his advocacy for Open Science on this work.

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Authors and Affiliations

  1. Computing & Mathematics Department, Oral Roberts University, Tulsa, OK, USA

    Brandon J. Jaquis, Ailin Li, Nolan D. Monnier, Robert G. Sisk & Andrew S. I. D. Lang

  2. Chemistry Department, University of North Texas, Denton, TX, USA

    William E. Acree Jr.

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  1. Brandon J. Jaquis
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  2. Ailin Li
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  3. Nolan D. Monnier
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  4. Robert G. Sisk
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  5. William E. Acree Jr.
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  6. Andrew S. I. D. Lang
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Correspondence to Andrew S. I. D. Lang.

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Jaquis, B.J., Li, A., Monnier, N.D. et al. Using Machine Learning to Predict Enthalpy of Solvation. J Solution Chem 48, 564–573 (2019). https://doi.org/10.1007/s10953-019-00867-1

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  • DOI: https://doi.org/10.1007/s10953-019-00867-1

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