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Random Forest Algorithm-Based Prediction of Solvation Gibbs Energies

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Abstract

Solvation Gibbs energy of chemicals is a critical parameter in chemical industry and chemical reactivity. Predicting the solvation Gibbs energies for a large number of solvents and solutes through machine learning techniques is challenging area. In this work, the random forest (RF) algorithm, together with a combined descriptor set from solvents and solutes, was used for developing a quantitative structure–property relationship (QSPR) model for solvation Gibbs energies of 6238 solute/solvent pairs. The optimal RF (ntree = 25, mtry = 10 and nodesize = 5) model was obtained, whose training and test sets, respectively, have determination coefficients of 0.935 and 0.924, and root mean square errors of 2.477 and 2.464 kJ·mol− 1. In predicting the solvation Gibbs energies for a large dataset, the optimal RF model is comparable to other QSPR models reported in the literature.

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The data that support the findings of this study are available in the supporting information of this article.

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Acknowledgements

This work was supported by the Open Project Program of Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration (Hunan Institute of Engineering) (No. 2018KF11) and the Hunan Provincial Natural Science Foundation (Nos. 2020JJ6013, 2021JJ50111).

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Authors and Affiliations

  1. Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, 411104, Hunan, China

    Meiping Liao, Feng Wu, Xinliang Yu, Le Zhao, Haojie Wu & Jiannan Zhou

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  1. Meiping Liao
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  2. Feng Wu
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  3. Xinliang Yu
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  4. Le Zhao
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  5. Haojie Wu
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Contributions

ML, LZ, HW, and JZ contributed to data collection and curation, descriptor calculation, software, and model development; FW contributed to manuscript revision; XY contributed to conceptualization, methodology, writing-original draft, and manuscript revision.

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Correspondence to Feng Wu or Xinliang Yu.

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Liao, M., Wu, F., Yu, X. et al. Random Forest Algorithm-Based Prediction of Solvation Gibbs Energies. J Solution Chem 52, 487–498 (2023). https://doi.org/10.1007/s10953-023-01247-6

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