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Updated: Jun 23, 2026

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Published on: September 5, 2014

Machine learning to design metal-organic frameworks: progress and challenges from a data efficiency perspective.

Diego A Gómez-Gualdrón^1,2, Tatiane Gercina de Vilas¹, Katherine Ardila¹

¹Department of Chemical and Biological Engineering, Colorado School of Mines, 1601 Illinois St, Golden, CO 80401, USA. dgomezgualdron@mines.edu.

Materials Horizons

|December 15, 2025

Summary

This summary is machine-generated.

Machine learning (ML) accelerates metal-organic framework (MOF) discovery by overcoming design challenges. This review explores efficient ML strategies for MOF property prediction and design, enhancing accessibility for researchers.

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Area of Science:

Materials Science
Computational Chemistry
Artificial Intelligence

Background:

Metal-organic frameworks (MOFs) offer design flexibility but present a vast search space, making traditional screening methods resource-intensive.
The growing availability of MOF data necessitates efficient computational approaches for accelerated discovery and design.

Purpose of the Study:

To critically review machine learning (ML) applications at the intersection of ML and MOFs.
To survey strategies for reducing data and resource burdens in MOF property prediction and design.
To identify future opportunities for ML-empowered MOF design.

Main Methods:

Feature engineering
Model architecture selection
Transfer learning
Active learning
Generative models

Main Results:

ML methods offer a promising solution to accelerate MOF discovery by addressing the large design space.
Various strategies, including feature engineering and generative models, can enhance data and resource efficiency.
Current ML applications primarily focus on MOF adsorption properties, with potential for broader applications.

Conclusions:

Efficient ML strategies are crucial for robust and accessible ML-aided MOF design.
Addressing data quality and scalability challenges will further advance ML in MOF research.
Future work should expand ML applications beyond adsorption properties to other MOF functionalities.