High-throughput screening of bimetallic catalysts enabled by machine learning

We present a holistic machine-learning framework for rapid screening of bimetallic catalysts with the aid of the descriptor-based kinetic analysis. A catalyst database, which contains the adsorption energies of *CO and *OH on {111}-terminated model alloy surfaces and fingerprint features of active s...

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Veröffentlicht in:	Journal of materials chemistry. A, Materials for energy and sustainability Materials for energy and sustainability, 2017, Vol.5 (46), p.24131-24138
Hauptverfasser:	Li, Zheng, Wang, Siwen, Chin, Wei Shan, Achenie, Luke E, Xin, Hongliang
Format:	Artikel
Sprache:	eng
Schlagworte:	Adsorbates Adsorption Artificial neural networks Bimetals Catalysis Catalysts Chemisorption Coordination compounds Density functional theory Fingerprints High-throughput screening Learning algorithms Machine learning Metals Neural networks Oxidation Scaling Screening Surface chemistry
Online-Zugang:	Volltext
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