Application of Machine Learning in High-throughput Screening of Binary Alloys for the Hydrogenation of Benzene

The hydrogenation of benzene is a key reaction in industry, and binary alloys are promising candidates for improving the catalytic efficiency of this process. In this study, the adsorption energies of benzene and hydrogen over random 150 alloys are determined using density functional theory (DFT) ca...

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Vydané v:	Catalysis letters Ročník 155; číslo 12; s. 400
Hlavní autori:	Chang, Zhili, Li, Guangquan, Cai, Wenjun, Liu, Haolan, Zhang, Guangcheng, Ou, Weitao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.12.2025 Springer Nature B.V
Predmet:	Adsorption Algorithms Alloys Benzene Binary alloys Catalysis Chemistry Chemistry and Materials Science Correlation coefficients Density functional theory Hydrocarbons Hydrogen Hydrogenation Industrial Chemistry/Chemical Engineering Machine learning Metals Multilayer perceptrons Neural networks Organometallic Chemistry Packaging Physical Chemistry Physical properties Ratios Root-mean-square errors Support vector machines Adsorption energy Benzene hydrogenation LGBM Machine learning Binary alloy
ISSN:	1011-372X, 1572-879X
On-line prístup:	Získať plný text
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