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 a new method for predicting the d-band centre value. They use readily available data, such as density and electronegativity from other metals or bimetals, to predict the d-band centre for 11 metals and their bimetallic alloys. The results compare favourably with values obtained through density functional theory.
To read the full article please visit Chemistry World.
Machine-learning prediction of the d-band center for metals and bimetals
Ichigaku Takigawa, Ken-ichi Shimizu, Koji Tsuda and Satoru Takakusagi
RSC Adv., 2016,6, 52587-52595
DOI: 10.1039/C6RA04345C, Paper
