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<p>Metal organic frameworks (MOFs) have demonstrated remarkable performance in hydrogen storage due to their unique properties, such as high gravimetric densities, rapid kinetics, and reversibility. This paper models hydrogen storage capacity of MOFs utilizing numerous machine learning approaches, such as the Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Gaussian Process Regression (GPR). Here, Radial Basic Function (RBF) and Rational Quadratic (RQ) kernel functions were employed in GPR. To this end, a comprehensive databank including 1729 experimental data points was compiled from various literature surveys. Temperature, pressure, surface area, and pore volume were utilized as input variables in this databank. The results indicate that the GPR-RQ intelligent model achieved superior performance, delivering highly accurate predictions with a mean absolute error (MAE) of 0.0036, Root Mean Square Error (RMSE) of 0.0247, and a correlation coefficient (R²) of 0.9998. In terms of RMSE values, the models GPR-RQ, GPR-RBF, CNN, and DNN were ranked in order of their performance, respectively. Moreover, by calculating Pearson correlation coefficient, the sensitivity analysis showed that pore volume and surface area emerged as the most influential factors in hydrogen storage, boasting absolute relevancy factors of 0.45 and 0.47, respectively. Lastly, outlier detection assessment employing the leverage approach revealed that almost 98% of the data points utilized in the modeling are reliable and fall within the valid range. This study contributed to understanding how input features collectively influence the estimation of hydrogen storage capacity of MOFs.</p>

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Exploring advanced artificial intelligence techniques for efficient hydrogen storage in metal organic frameworks

  • 期刊论文

  • 发表日期：

    2025年1月31日

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  • Arefeh Naghizadeh,
  • Fahimeh Hadavimoghaddam,
  • Saeid Atashrouz,
  • Meriem Essakhraoui,
  • Dragutin Nedeljkovic,
  • Abdolhossein Hemmati-Sarapardeh,
  • Ahmad Mohaddespour

摘要

Metal organic frameworks (MOFs) have demonstrated remarkable performance in hydrogen storage due to their unique properties, such as high gravimetric densities, rapid kinetics, and reversibility. This paper models hydrogen storage capacity of MOFs utilizing numerous machine learning approaches, such as the Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Gaussian Process Regression (GPR). Here, Radial Basic Function (RBF) and Rational Quadratic (RQ) kernel functions were employed in GPR. To this end, a comprehensive databank including 1729 experimental data points was compiled from various literature surveys. Temperature, pressure, surface area, and pore volume were utilized as input variables in this databank. The results indicate that the GPR-RQ intelligent model achieved superior performance, delivering highly accurate predictions with a mean absolute error (MAE) of 0.0036, Root Mean Square Error (RMSE) of 0.0247, and a correlation coefficient (R²) of 0.9998. In terms of RMSE values, the models GPR-RQ, GPR-RBF, CNN, and DNN were ranked in order of their performance, respectively. Moreover, by calculating Pearson correlation coefficient, the sensitivity analysis showed that pore volume and surface area emerged as the most influential factors in hydrogen storage, boasting absolute relevancy factors of 0.45 and 0.47, respectively. Lastly, outlier detection assessment employing the leverage approach revealed that almost 98% of the data points utilized in the modeling are reliable and fall within the valid range. This study contributed to understanding how input features collectively influence the estimation of hydrogen storage capacity of MOFs.