Machine-learning accelerates catalytic trend spotting

Researchers in Japan have used a machine-learning method to cut the time it takes to predict the catalytic potential of different metals. Binding between a metal surface and an adsorbate mainly depends on the electronic structure of the metal. More energy at centre of the metal’s d-band creates a stronger bond between its surface and the adsorbate. Based on this theory, scientists have long regarded a value called the d-band centre as a key indicator of a metal’s catalytic activity. Machine learning helps researchers tackle challenging tasks, such as designing pollution filter catalysts at industrial scale © iStock Researchers normally compute this value independently for each metal using first-principles calculations. Now, as part of a wider interest in machine-learning applications, Ichigaku Takigawa and his group at Hokkaido University have developed…


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