A machine learning technique rapidly rediscovered rules governing catalysts that took humans years of difficult calculations to reveal—and even explained a deviation. The University of Michigan team that developed the technique believes other researchers will be able to use it to make faster progress in designing materials for a variety of purposes.
Click here for original story, Machine learning homes in on catalyst interactions to accelerate materials development
Source: Phys.org