AI-driven advances in metal-organic frameworks: from data to design and applications

Area of Science:

• Materials Science
• Computational Chemistry
• Artificial Intelligence

Background:

• Metal-organic frameworks (MOFs) offer tunable properties for applications like gas storage and carbon capture.
• The vast chemical space of MOFs challenges traditional discovery methods.
• AI and ML are emerging as powerful tools to overcome these limitations.

Purpose of the Study:

• To provide a comprehensive review of AI-driven strategies for accelerating MOF research.
• To highlight key AI techniques and their impact on MOF design and screening.
• To discuss current challenges and future directions in AI-accelerated MOF innovation.

Main Methods:

• Review of AI/ML techniques including deep learning, graph neural networks, and generative models.
• Discussion of AI-simulation frameworks and integration with robotics.
• Analysis of relevant databases and data protocols.

Main Results:

• AI/ML significantly enhances MOF property prediction, structure generation, and synthesis planning.
• Graph neural networks and active learning (AL) drive breakthroughs in structure-property relationships.
• Integration with robotics paves the way for autonomous MOF laboratories.

Conclusions:

• AI offers transformative potential for rapid MOF discovery and optimization.
• Addressing challenges in data quality, interpretability, and validation is crucial for future progress.
• Physics-informed ML and enhanced AI-robotics integration represent key future research avenues.