Machine Learning Links Material Composition and Performance in Catalysts
August 24, 2021
August 24, 2021
ANN ARBOR, Michigan, Aug. 24 (TNSJou) -- The University of Michigan issued the following news release:
In a finding that could help pave the way toward cleaner fuels and a more sustainable chemical industry, researchers at the University of Michigan have used machine learning to predict how the compositions of metal alloys and metal oxides affect their electronic structures.
The electronic structure is key to understanding how the material will perform as a mediator, or . . .
In a finding that could help pave the way toward cleaner fuels and a more sustainable chemical industry, researchers at the University of Michigan have used machine learning to predict how the compositions of metal alloys and metal oxides affect their electronic structures.
The electronic structure is key to understanding how the material will perform as a mediator, or . . .