Unlocking the Fine Structure of Catalysis with a Sensitive Deep Learning Framework
A new framework that is sensitive to fine structure has been developed by researchers at the University of California, Los Angeles (UCLA). The framework is designed to be used for deep catalytic modeling and could lead to more efficient and accurate catalytic processes. The research team used machine learning algorithms to develop the framework, which is capable of capturing the subtle differences between different catalytic reactions. This approach could lead to better predictions of how catalysts interact with different chemical reactants, leading to improved efficiency and accuracy in the laboratory. Ultimately, this could lead to more efficient and cost-effective industrial processes.
source: Phys.org