A machine learning framework for accelerating the development of highly efficient methanol synthesis catalysts

IF 13.1 1区 化学 Q1 Energy
Weixian Li , Yi Dong , Mingchu Ran , Saisai Lin , Peng Liu , Hao Song , Jundong Yi , Chaoyang Zhu , Zhifu Qi , Chenghang Zheng , Xiao Zhang , Xiang Gao
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引用次数: 0

Abstract

Converting CO2 with green hydrogen to methanol as a carbon-neutral liquid fuel is a promising route for the long-term storage and distribution of intermittent renewable energy. Nevertheless, attaining highly efficient methanol synthesis catalysts from the vast composition space remains a significant challenge. Here we present a machine learning framework for accelerating the development of high space-time yield (STY) methanol synthesis catalysts. A database of methanol synthesis catalysts has been compiled, consisting of catalyst composition, preparation parameters, structural characteristics, reaction conditions and their corresponding catalytic performance. A methodology for constructing catalyst features based on the intrinsic physicochemical properties of the catalyst components has been developed, which significantly reduced the data dimensionality and enhanced the efficiency of machine learning operations. Two high-precision machine learning prediction models for the activities and product selectivity of catalysts were trained and obtained. Using this machine learning framework, an efficient search was achieved within the catalyst composition space, leading to the successful identification of high STY multi-element oxide methanol synthesis catalysts. Notably, the CuZnAlTi catalyst achieved high STYs of 0.49 and 0.65 gMeOH/(gcatalyst h) for CO2 and CO hydrogenation to methanol at 250 °C, respectively, and the STY was further increased to 2.63 gMeOH/(gcatalyst h) in CO and CO2 co-hydrogenation.
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来源期刊
Journal of Energy Chemistry
Journal of Energy Chemistry CHEMISTRY, APPLIED-CHEMISTRY, PHYSICAL
CiteScore
19.10
自引率
8.40%
发文量
3631
审稿时长
15 days
期刊介绍: The Journal of Energy Chemistry, the official publication of Science Press and the Dalian Institute of Chemical Physics, Chinese Academy of Sciences, serves as a platform for reporting creative research and innovative applications in energy chemistry. It mainly reports on creative researches and innovative applications of chemical conversions of fossil energy, carbon dioxide, electrochemical energy and hydrogen energy, as well as the conversions of biomass and solar energy related with chemical issues to promote academic exchanges in the field of energy chemistry and to accelerate the exploration, research and development of energy science and technologies. This journal focuses on original research papers covering various topics within energy chemistry worldwide, including: Optimized utilization of fossil energy Hydrogen energy Conversion and storage of electrochemical energy Capture, storage, and chemical conversion of carbon dioxide Materials and nanotechnologies for energy conversion and storage Chemistry in biomass conversion Chemistry in the utilization of solar energy
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