Transforming Catalysis with Machine Learning: Emerging Tools and Next-Gen Strategies

Area of Science:

• Chemical Engineering
• Materials Science
• Computational Chemistry

Background:

• Catalysis is crucial for the chemical industry, but traditional methods for discovering new catalysts are slow.
• Machine learning (ML) offers a powerful, efficient approach to accelerate catalyst development.

Purpose of the Study:

• To provide a comprehensive overview of ML applications in catalysis.
• To discuss challenges and future directions for ML in catalyst design and reaction prediction.

Main Methods:

• Review of traditional machine learning and deep learning (DL) techniques.
• Analysis of modeling strategies, algorithmic frameworks, and applications in catalyst design, reaction prediction, and surface adsorption.
• Discussion of data challenges, interpretability, and integration with experimental workflows.

Main Results:

• ML, particularly DL, shows significant promise in accelerating catalyst discovery and development.
• Key applications include catalyst design, reaction prediction, and modeling surface adsorption phenomena.
• Current challenges involve data quality, model interpretability, and experimental integration.

Conclusions:

• ML is transforming catalytic chemistry, offering unprecedented efficiency in catalyst development.
• Addressing data fragmentation, interpretability, and workflow integration is key to advancing ML in catalysis.