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December 8, 2025Materials Horizons12 citationsOpen Access

Machine Learning to Design Metal-Organic Frameworks: Progress and Challenges from a Data Efficiency Perspective

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DGDiego Gomez GualdronTVTatiane Gercina de VilasKAKatherine Ardila

Key Points

  • This review examines the integration of machine learning into the design of metal-organic frameworks.
  • Analyzes existing literature at the intersection of machine learning and metal-organic frameworks.
  • Evaluates the modular design of MOFs for flexibility and usability.
  • Highlights challenges in achieving data efficiency in machine learning applications to MOF design.
  • Summarizes progress made in utilizing ML to optimize MOF architectures.

Abstract

This review critically examines work at the intersection of machine learning (ML) and metal-organic frameworks (MOFs). The modular nature of MOFs enables immense design flexibility and applicability to a wide...

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Cite This Study

Gualdron et al. (2025) studied this question.

synapsesocial.com/papers/69362f444fa91c937236d56bhttps://doi.org/10.1039/d5mh01467k
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