Machine Learning Descriptors for Data-Driven Catalysis Study.

Area of Science:

• Catalysis
• Materials Science
• Computational Chemistry

Background:

• Traditional experimental and theoretical methods struggle to optimize catalytic processes and discover novel catalysts.
• Machine learning (ML) offers a powerful approach to accelerate catalyst research and development.
• Effective input features, known as descriptors, are crucial for ML model accuracy and identifying key factors influencing catalysis.

Purpose of the Study:

• To review tactics for utilizing and extracting catalytic descriptors in ML-assisted research.
• To discuss the advantages and limitations of various descriptors.
• To highlight new spectral descriptors and a paradigm for integrating computational and experimental ML models.

Main Methods:

• Review of existing literature on catalytic descriptors and ML applications in catalysis.
• Analysis of descriptor effectiveness, advantages, and limitations.
• Introduction of novel spectral descriptors and integrated ML approaches.

Main Results:

• Descriptors significantly enhance ML model predictive accuracy in catalysis.
• Newly developed spectral descriptors show promise for predicting catalytic performance.
• A novel research paradigm integrates computational and experimental ML models via intermediate descriptors.

Conclusions:

• Strategic selection and extraction of descriptors are key to advancing ML in catalysis.
• The integration of computational and experimental ML models offers new avenues for catalyst discovery.
• Addressing current challenges and future perspectives will further propel ML-driven catalysis research.