High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery.

Area of Science:

• Catalysis and Materials Science
• Computational Chemistry and Data Science

Background:

• High-throughput experimentation (HTE) generates large, reproducible datasets for catalyst formulation.
• Current knowledge extraction relies on statistical methods, primarily for optimization.
• Advanced machine learning (ML) offers potential for predictive discovery of novel catalysts.

Purpose of the Study:

• To explore the synergy between HTE and advanced ML for accelerating catalyst discovery.
• To provide examples of ML applications in catalyst design and optimization.
• To offer an outlook on future ML methodologies in materials discovery.

Main Methods:

• Statistical design of experiments for catalyst synthesis.
• Genetic algorithms for catalyst formulation optimization.
• Random forest ML models trained on HTE data for novel catalyst discovery.

Main Results:

• Demonstration of ML's capability to move beyond statistical optimization.
• Examples showcasing the application of ML in identifying new catalyst candidates.
• Highlighting the potential of ML for predictive materials discovery.

Conclusions:

• The integration of HTE and advanced ML significantly accelerates the discovery of novel catalysts.
• ML methodologies offer a powerful paradigm shift from traditional statistical approaches in catalysis.
• Future research should focus on advanced ML for predictive materials discovery using experimental data.