Accelerating the discovery and optimization of metal-organic framework materials via machine learning

Area of Science:

• Materials Science
• Computational Chemistry
• Chemical Engineering

Background:

• Metal-organic frameworks (MOFs) are advanced porous materials with diverse applications.
• Traditional MOF synthesis and optimization are slow and expensive.
• Machine learning (ML) offers a promising approach to overcome these limitations.

Purpose of the Study:

• To systematically review the application of ML in MOF research.
• To cover ML algorithms, data handling, model evaluation, and interpretability.
• To highlight ML's role in accelerating MOF design, screening, and performance prediction.

Main Methods:

• Review of common ML algorithms (regression, classification, clustering, deep learning, reinforcement learning).
• Discussion of data acquisition, preprocessing, and model evaluation techniques.
• Analysis of ML applications in MOF material design and property prediction.

Main Results:

• ML significantly speeds up MOF research and development.
• Integration of ML with MOF science provides a comprehensive perspective.
• ML aids in structure-property relationship analysis and performance prediction.

Conclusions:

• ML is pivotal in advancing MOF research, design, and application.
• Interdisciplinary collaboration is essential for future progress.
• ML tools can guide experimental scientists, promoting green chemistry and sustainability.