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A Quantitative Structure–Property Relationship Model for Surface Tension Based on Artificial Neural Network

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Abstract

In this study, an artificial neural network (ANN) model was developed based on molecular descriptors to predict the surface tension of liquids. A dataset containing various features was constructed by collecting experimental data from 25 different fluids and extracting molecular structural descriptors. Feature selection was performed using the forward search wrapper method based on Random Forest, identifying 7 significant features (Temperature, MinAbsEStateIndex, LabuteASA, MolMR, Chi1v, qed and FpDensityMorgan3) for surface tension prediction. Subsequently, an ANN model was constructed with the selected features as inputs to predict the surface tension of liquids. The derived model demonstrates high accuracy with a correlation coefficient (R) exceeding 0.999 and a notably low mean square error (MSE = 1.843e−5). Moreover, the ANN model exhibited a total average absolute deviation (AAD) of 0.98 %, comparable to that of the REFPROP, which had a total AAD of 1.26 %. This quantitative model serves an easy tool for gaining insights into the molecular underpinnings of surface tension and predicting its value across various fluids.

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No datasets were generated or analysed during the current study.

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Funding

This work is financially supported by the National Natural Science Foundation of China (Nos. 52276020, 52376212).

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Authors and Affiliations

  1. Ningbo Research Institute, Zhejiang University, Ningbo, 315100, China

    Nian Li & Guangming Chen

  2. NingboTech University, Ningbo, 315100, China

    Nian Li, Neng Gao & Guangming Chen

  3. School of Mechanical & Materials Engineering, University College Dublin, Dublin, D04 V1W8, Ireland

    Xuehui Wang

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  1. Nian Li
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Contributions

N.G. designed research; N.L. performed research; N.G., N.L., X.H.W., and G.M.C. analyzed data; N.L., X.H.W., and N.G. wrote the paper.

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Correspondence to Neng Gao.

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Appendix

Appendix

  1. 1.

    The mapminmax struct for inputs

    name

    'mapminmax'

    xrows

    7

    xmax

    [423.1500;6;50.6279;21.5490;2.1649;0.4895;2]

    xmin

    [28.6300;0;8.7393;0;0;0.3516;0.8889]

    xrange

    [394.5200;6;41.88862;21.5490;2.1649;0.1379;1.1111]

    yrows

    7

    ymax

    1

    ymin

    0

    yrange

    1

    gain

    [0.0025;0.16667;0.0239;0.0464;0.4619;7.2502;0.9000]

    xoffset

    [28.6300;0;8.7393;0;0;0.3516;0.8889]

    no_change

    0

  2. 2.

    The mapminmax struct for output

    name

    'mapminmax'

    xrows

    1

    xmax

    25.9000

    xmin

    0.3800

    xrange

    25.5200

    yrows

    1

    ymax

    1

    ymin

    0

    yrange

    1

    gain

    0.0392

    xoffset

    0.3800

    no_change

    0

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Li, N., Wang, X., Gao, N. et al. A Quantitative Structure–Property Relationship Model for Surface Tension Based on Artificial Neural Network. Int J Thermophys 45, 106 (2024). https://doi.org/10.1007/s10765-024-03398-0

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