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This study introduces a machine learning (ML) method using convolutional neural networks to predict gas adsorption from raw N2 adsorption data. The ML model accurately forecasts Ar adsorption, even in complex pore structures.

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Area of Science:

  • Materials Science
  • Physical Chemistry
  • Computational Chemistry

Background:

  • Predicting gas adsorption from pore structure is common but challenging due to complex real-world data.
  • Traditional methods often rely on approximate structural information from characterization data.

Purpose of the Study:

  • To develop a machine learning (ML) method for predicting gas adsorption directly from raw N2 adsorption characterization data.
  • To overcome limitations of traditional methods in handling complex pore structures.

Main Methods:

  • A convolutional neural network (CNN) model was developed for gas adsorption prediction.
  • The ML model was trained using extensive data generated from classical density functional theory (DFT) calculations.
  • The model was trained on slit pore data but tested for broader applicability.

Main Results:

  • The ML model achieved highly accurate predictions for Argon (Ar) adsorption.
  • The model demonstrated successful application to three-dimensional structured pores and real-world materials, beyond its training data.
  • Strong agreement was observed between predicted and actual adsorption isotherms.

Conclusions:

  • Machine learning can effectively predict gas adsorption from raw N2 adsorption data, bypassing complex structural analysis.
  • A universal relationship exists among adsorption isotherms of different adsorbates, which can be captured by ML models.
  • This approach offers a powerful tool for materials characterization and gas adsorption studies.