Machine Learning in Catalysis, From Proposal to Practicing.

Area of Science:

• Catalysis
• Computational Chemistry
• Materials Science

Background:

• Machine learning (ML) methods are increasingly powerful predictive tools across various scientific and industrial fields.
• The application of ML in catalysis remains relatively underdeveloped despite its potential.
• Rapid advancements in ML algorithms necessitate their exploration in catalysis research.

Purpose of the Study:

• To review the current applications of ML in homogeneous and heterogeneous catalysis.
• To highlight the potential of ML for accelerating catalyst discovery and optimization.
• To emphasize the role of ML in extracting mechanistic insights from catalytic data.

Main Methods:

• Review of existing literature on ML applications in catalysis.
• Discussion of various ML approaches employed in catalytic studies.
• Analysis of how ML models extract information from catalytic data.

Main Results:

• ML is shown to be a potent tool for predictive modeling in catalysis.
• Current ML applications span both homogeneous and heterogeneous catalytic systems.
• ML facilitates computational optimization and mechanistic understanding.

Conclusions:

• ML offers a significant opportunity to advance catalysis research and development.
• Statistical learning techniques are crucial for computational catalyst design and discovery.
• ML can effectively translate data and precatalyst information into optimized catalytic systems.