Deep Learning Guided Exploration of Transition Metal Oxide Catalysts in Acetylene Selective Hydrogenation

The development of empirical materials, hindered by the complex interplay between material properties and reaction mechanisms, typically necessitates an extensive trial-and-error process to identify optimal catalysts. In our study, we integrate density functional theory (DFT) predictions to generate mappings of electronic and molecular adsorption properties, which are then employed to identify novel materials using deep learning algorithms. These newly identified materials were synthesized and assessed for their catalytic performance in the selective hydrogenation of acetylene. Notably, CuTiO<sub>3</sub> catalysts demonstrated exceptional performance, achieving acetylene conversion exceeding 99% and ethylene selectivity greater than 99% at a relatively low temperature of 75 °C. Additionally, CuO-doped TiO<sub>2</sub> was observed to form strong acetylene adsorption sites and weaker ethylene adsorption sites (Cu-O-Ti). The p-π hybridized coupling between the oxygen p-orbitals and the π-electrons of acetylene in the Cu-O-Ti structure was found to play a critical role in facilitating the conversion of acetylene to ethylene.

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Publication Details

Journal
Journal of the American Chemical Society
Published
2025-12-20
DOI
https://doi.org/10.1021/jacs.5c16333
Citations
2
Primary Topic
Catalytic Processes in Materials Science
Type
article
Field-Weighted Citation Impact
1.04
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article

Deep Learning Guided Exploration of Transition Metal Oxide Catalysts in Acetylene Selective Hydrogenation

Jinghui Lyu, Ying Zheng, Lili Lin, Qunfeng Zhang et al.
2 citations
Journal of the American Chemical Society
Catalytic Processes in Materials Science
1.04
article

Deep Learning Guided Exploration of Transition Metal Oxide Catalysts in Acetylene Selective Hydrogenation

Jinghui Lyu, Ying Zheng, Lili Lin, Qunfeng Zhang, Chunshan Lu, Jianguo Wang, Feng Feng, Xinhui Zhang, Jie Luo, Qingtao Wang, Chong Yao, Qianjun Zhang, Yuanjing Fan, Xiaonian Li, Hao Lu, Hao Liu, Rubo Fang
article en
2 citations

Abstract

The development of empirical materials, hindered by the complex interplay between material properties and reaction mechanisms, typically necessitates an extensive trial-and-error process to identify optimal catalysts. In our study, we integrate density functional theory (DFT) predictions to generate mappings of electronic and molecular adsorption properties, which are then employed to identify novel materials using deep learning algorithms. These newly identified materials were synthesized and assessed for their catalytic performance in the selective hydrogenation of acetylene. Notably, CuTiO<sub>3</sub> catalysts demonstrated exceptional performance, achieving acetylene conversion exceeding 99% and ethylene selectivity greater than 99% at a relatively low temperature of 75 °C. Additionally, CuO-doped TiO<sub>2</sub> was observed to form strong acetylene adsorption sites and weaker ethylene adsorption sites (Cu-O-Ti). The p-π hybridized coupling between the oxygen p-orbitals and the π-electrons of acetylene in the Cu-O-Ti structure was found to play a critical role in facilitating the conversion of acetylene to ethylene.

Journal of the American Chemical SocietyVol. 148(1)
Western University (CA), Wenzhou-Kean University (CN), State Key Laboratory of Chemical Engineering (CN), Chemical Synthesis Lab (SG), PRG S&Tech (South Korea), Zhejiang University of Technology (CN), Ocean University of China (CN)
Openalex Percentile: Top 21%
Catalytic Processes in Materials Science
1.04
DOISourceOpenAlex
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