Machine learning integrated photocatalysis: progress and challenges

Area of Science:

• Photocatalysis and Materials Science
• Artificial Intelligence in Chemistry

Background:

• Traditional photocatalyst discovery relies on serendipity and trial-and-error.
• Machine learning (ML) offers a promising avenue to accelerate the discovery of efficient photocatalysts.
• Significant challenges remain in integrating ML with photocatalysis.

Purpose of the Study:

• To systematically review advances in ML-integrated photocatalysis.
• To explore ML applications in understanding photoactivity-structure relationships.
• To outline a future paradigm for photocatalysis using AI, robotics, and automation.

Main Methods:

• Review of ML applications across key photocatalytic processes: light harvesting, charge generation/separation, and surface redox reactions.
• Analysis of ML's role in elucidating complex photoactivity-structure relationships.
• Discussion of AI, robotics, and automation integration for future photocatalysis.

Main Results:

• ML integration enhances understanding of photocatalytic mechanisms.
• ML aids in identifying key factors governing photocatalyst activity.
• Progress is being made in applying ML to solar fuel synthesis.

Conclusions:

• ML-integrated photocatalysis is a rapidly advancing field with significant potential for solar fuel synthesis.
• Addressing current challenges is crucial for realizing the full potential of AI in photocatalysis.
• This review provides a roadmap for researchers in photocatalysis and artificial intelligence.