Catalyst Design

Catalyst design aims to optimize catalyst structure and composition for enhanced efficiency and selectivity in chemical reactions. Current research heavily utilizes machine learning, employing various architectures like graph neural networks, large language models, and Bayesian optimization to analyze high-throughput experimental data, predict catalytic properties, and accelerate the discovery of novel materials. These advancements are significantly impacting the speed and efficiency of catalyst development, with applications ranging from sustainable energy production to pharmaceutical synthesis. Explainable AI techniques are also gaining traction to improve the interpretability of these complex models and provide chemical insights.