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Related Experiment Video

Updated: Apr 7, 2026

Author Spotlight: Characterizing Porous Materials for Aiding the Development of Robust Metal-Organic Frameworks with Adsorption Behavior
06:45

Author Spotlight: Characterizing Porous Materials for Aiding the Development of Robust Metal-Organic Frameworks with Adsorption Behavior

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Data-driven explainable artificial intelligence models for predicting CO2 uptake in metal-organic frameworks.

Mohd Azfar Shaida1, Laiba Saleem2, Syed Ali Waqas Ahmad3

  • 1Department of Industrial Chemistry, Aligarh Muslim University, Aligarh, 202002, UP, India.

Environmental Science and Pollution Research International
|April 6, 2026
PubMed
Summary
This summary is machine-generated.

This study uses machine learning (ML) and explainable AI (XAI) to predict CO2 uptake in metal-organic frameworks (MOFs). Gradient Boosting models accurately predict CO2 adsorption, identifying pressure and pore volume as key factors.

Keywords:
CO2 uptakeExplainable artificial intelligenceGraphical user interfaceMachine learningMetal–organic frameworkSHAP

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Machine Learning

Background:

  • Metal-organic frameworks (MOFs) show promise for carbon capture.
  • Predicting CO2 uptake in MOFs is crucial for material design.
  • Existing methods lack comprehensive prediction and interpretation frameworks.

Purpose of the Study:

  • Develop a data-driven framework for predicting CO2 uptake in MOFs.
  • Employ machine learning (ML) and explainable artificial intelligence (XAI) for prediction and interpretation.
  • Create a user-friendly tool for MOF screening in carbon capture applications.

Main Methods:

  • Trained four supervised ML models (ANN, RF, AdB, GB) on a dataset of 223 MOF samples.
  • Utilized Gradient Boosting (GB) for highest predictive accuracy.
  • Applied SHapley Additive exPlanations (SHAP) for model interpretability.

Main Results:

  • The GB model demonstrated high accuracy in predicting CO2 uptake (R²=0.99 training, R²=0.982 testing).
  • SHAP analysis identified pressure (P) and pore volume (PV) as dominant features influencing CO2 adsorption.
  • Temperature (T) had minimal impact on CO2 uptake predictions.

Conclusions:

  • The developed framework integrates prediction, interpretation, and deployment for MOF screening.
  • This tool supports data-driven advancements in carbon capture materials.
  • The framework offers a practical and transparent approach for designing MOFs with enhanced CO2 uptake.