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<p>The ‘Quantitative Structure–Property Relationship’ (QSPR) method has been used for the prediction of solubility of hydrogen (x) in different chemicals. The dataset consists of 3761 datapoints including 100 unique chemicals at the wide ranges of T and P. An MLR-model, the simplest form of machine learning algorithm, was constructed using the selected descriptors to predict x in various chemicals. For the first time, a comprehensive and predictive MLR-based QSPR model has been developed for this target. The dataset was divided into a training set including 2570 datapoints and to a test set including 1191 datapoints. The advantages of this approach are thoroughly discussed and compared with other available models which were developed with other ML algorithms. Unlike previous models, internal validation was performed on the MLR-QSPR model. According to the results of statistical parameters (R<sup>2</sup> = 0.96 and Q<sup>2</sup><sub>LOO-CV</sub> = 0.96), the predictive capability of the MLR-QSPR model was acceptable for training set.</p>

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Prediction of solubility of hydrogen in chemicals using QSPR-based machine learning approach: a comparative study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Ali Ebrahimpoor Gorji,
  • Ville Alopaeus

摘要

The ‘Quantitative Structure–Property Relationship’ (QSPR) method has been used for the prediction of solubility of hydrogen (x) in different chemicals. The dataset consists of 3761 datapoints including 100 unique chemicals at the wide ranges of T and P. An MLR-model, the simplest form of machine learning algorithm, was constructed using the selected descriptors to predict x in various chemicals. For the first time, a comprehensive and predictive MLR-based QSPR model has been developed for this target. The dataset was divided into a training set including 2570 datapoints and to a test set including 1191 datapoints. The advantages of this approach are thoroughly discussed and compared with other available models which were developed with other ML algorithms. Unlike previous models, internal validation was performed on the MLR-QSPR model. According to the results of statistical parameters (R2 = 0.96 and Q2LOO-CV = 0.96), the predictive capability of the MLR-QSPR model was acceptable for training set.