Chemists at the U.S. Department of Energy’s Brookhaven National Laboratory have developed a new machine-learning (ML) framework that can zero in on which steps of a multistep chemical conversion should be tweaked to improve productivity. The approach could help guide the design of catalysts—chemical “dealmakers” that speed up reactions.
Click here for original story, Machine learning framework IDs targets for improving catalysts
Source: Phys.org