Ge-Hao Liang , Heng-Su Liu , Xi-Ming Zhang , Jian-Feng Li , Shisheng Zheng
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引用次数: 0
Abstract
Metal-nitrogen-carbon (M-N-C) single-atom catalysts are widely utilized in various energy-related catalytic processes, offering a highly efficient and cost-effective catalytic system with significant potential. Recently, curvature-induced strain has been extensively demonstrated as a powerful tool for modulating the catalytic performance of M-N-C catalysts. However, identifying optimal strain patterns using density functional theory (DFT) is computationally intractable due to the high-dimensional search space. Here, we developed a graph neural network (GNN) integrated with an advanced topological data analysis tool—persistent homology—to predict the adsorption energy response of adsorbate under proposed curvature patterns, using nitric oxide electroreduction (NORR) as an example. Our machine learning model achieves high accuracy in predicting the adsorption energy response to curvature, with a mean absolute error (MAE) of 0.126 eV. Furthermore, we elucidate general trends in curvature-modulated adsorption energies of intermediates across various metals and coordination environments. We recommend several promising catalysts for NORR that exhibit significant potential for performance optimization via curvature modulation. This methodology can be readily extended to describe other non-bonded interactions, such as lattice strain and surface stress, providing a versatile approach for advanced catalyst design.
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