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Machine-learning prediction of the d-band center for metals and bimetals

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Title: Machine-learning prediction of the d-band center for metals and bimetals
Authors: Takigawa, Ichigaku Browse this author →KAKEN DB
Shimizu, Ken-ichi Browse this author
Tsuda, Koji Browse this author
Takakusagi, Satoru Browse this author
Issue Date: May-2016
Publisher: Royal Society of Chemistry
Journal Title: RSC advances
Volume: 6
Issue: 58
Start Page: 52587
End Page: 52595
Publisher DOI: 10.1039/c6ra04345c
Abstract: The d-band center for metals has been widely used in order to understand activity trends in metal-surface-catalyzed reactions in terms of the linear Bronsted-Evans-Polanyi relation and Hammer-Norskov d-band model. In this paper, the d-band centers for eleven metals (Fe, Co, Ni, Cu, Ru, Rh, Pd, Ag, Ir, Pt, Au) and their pairwise bimetals for two different structures (1% metal doped- or overlayer-covered metal surfaces) are statistically predicted using machine learning methods from readily available values as descriptors for the target metals (such as the density and the enthalpy of fusion of each metal). The predictive accuracy of four regression methods with different numbers of descriptors and different test-set/training-set ratios are quantitatively evaluated using statistical cross validations. It is shown that the d-band centers are reasonably well predicted by the gradient boosting regression (GBR) method with only six descriptors, even when we predict 75% of the data from only 25% given for training (average root mean square error (RMSE) < 0.5 eV). This demonstrates a potential use of machine learning methods for predicting the activity trends of metal surfaces with a negligible CPU time compared to first-principles methods.
Type: article (author version)
URI: http://hdl.handle.net/2115/65614
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 瀧川 一学

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