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September 30, 2025Journal of Chemical Information and Modeling

Graph-Based Machine Learning Framework for Predicting Hydrogen Storage Capacity in Metal–Organic Frameworks

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Authors

AAAzzam AlfarrajKing Fahd University of Petroleum and MineralsMAMonther Rashed AlfuraidanKing Fahd University of Petroleum and MineralsAPAbdul Malik P. PeedikakkalKing Fahd University of Petroleum and Minerals

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Implication

This framework utilizes spectral graph theory to improve hydrogen uptake predictions in MOFs, indicating potential for advanced materials design.

Key Points

  • The framework predicts hydrogen uptake in metal-organic frameworks, achieving a maximum R2 of 0.737.
  • Using 3300 MOFs from the Cambridge Structural Database, the XGBoost regressor was the most effective model for prediction.
  • It employs graph-based descriptors that assess topological and geometric aspects of molecular structures for accurate modeling.
  • This framework not only predicts outcomes but also supports the design of new materials for hydrogen storage applications.

Cite This Study

Alfarraj et al. (2025) studied this question.

synapsesocial.com/papers/68dc1e358a7d58c25ebb1648https://doi.org/10.1021/acs.jcim.5c01528
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Also Consider

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