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Related Experiment Video

Updated: Sep 16, 2025

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Application of Soft Computing Represented by Regression Machine Learning Model and Artificial Lemming Algorithm in

Jiamin Zhang1, Yanzhe Li2, Chuanqi Li3

  • 1SINOPEC Research Institute of Petroleum Engineering, Beijing 100101, China.

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|July 12, 2025
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Summary

Machine learning models predict metal-organic framework (MOF) hydrogen storage capacity. The artificial lemming algorithm optimized random forest (ALA-RF) model demonstrated superior predictive performance, identifying pressure as a key factor.

Keywords:
artificial lemming algorithmhydrogen storagemachine learningmetal-organic frameworksprediction

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Computational Chemistry

Background:

  • Metal-organic frameworks (MOFs) possess unique properties making them promising for hydrogen storage applications.
  • Accurate prediction of hydrogen storage capacity in MOFs is crucial for material selection and process optimization.

Purpose of the Study:

  • To develop and evaluate several regression-based machine learning models for predicting hydrogen storage capacity in MOFs.
  • To optimize machine learning model hyperparameters using the artificial lemming algorithm (ALA).

Main Methods:

  • Development of artificial neuron network (ANN), support vector regression (SVR), random forest (RF), extreme learning machine (ELM), kernel extreme learning machine (KELM), and generalized regression neural network (GRNN) models.
  • Hyperparameter optimization using the artificial lemming algorithm (ALA).
  • Model training and testing using experimental hydrogen storage data, with performance evaluation via statistical metrics, regression plots, and Taylor graphs.

Main Results:

  • The ALA-optimized random forest (ALA-RF) model exhibited the highest predictive accuracy.
  • Optimal performance metrics for ALA-RF include R² of 0.9845 (training) and 0.9840 (testing), RMSE of 0.2719 (training) and 0.2828 (testing).
  • Pressure was identified as the most influential feature for predicting hydrogen storage capacity in MOFs.

Conclusions:

  • The ALA-RF model offers a robust and accurate approach for predicting hydrogen storage in MOFs.
  • These findings facilitate intelligent selection of MOFs and optimization of hydrogen storage operational conditions.