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http://hdl.handle.net/10397/105738
Title: | Direct machine learning predictions of C₃ pathways | Authors: | Sun, M Huang, B |
Issue Date: | 5-Apr-2024 | Source: | Advanced energy materials, 5 Apr. 2024, v. 14, no. 13, 2400152 | Abstract: | The C₃ pathways of CO₂ reduction reaction (CO₂RR) lead to the generation of high-value-added chemicals for broad industrial applications, which are still challenging for current electrocatalysis. Only limited electrocatalysts have been reported with the ability to achieve C₃ products while the corresponding reaction mechanisms are highly unclear. To overcome such challenges, the first-principle machine learning (FPML) technique on graphdiyne-based atomic catalysts (GDY-ACs) is introduced to directly predict the reaction trends for the key C─C─C coupling processes and the conversions to different C₃ products for the first time. All the prediction results are obtained only based on the learning dataset constructed by density functional theory (DFT) calculation results for C₁ and C₂ pathways, offering an efficient approach to screen promising electrocatalyst candidates for varied C₃ products. More importantly, the ML predictions not only reveal the significant role of the neighboring effect and the small–large integrated cycle mechanisms but also supply important insights into the C─C─C coupling processes for understanding the competitive reactions among C₁ to C₃ pathways. This work has offered an advanced breakthrough for the complicated CO₂RR processes, accelerating the future design of novel ACs for C₃ products with high efficiency and selectivity. | Keywords: | C₃ products C─C─C coupling Machine learning Neighboring effect Small–large integrated cycles |
Publisher: | Wiley-VCH | Journal: | Advanced energy materials | ISSN: | 1614-6832 | EISSN: | 1614-6840 | DOI: | 10.1002/aenm.202400152 | Rights: | © 2024 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. The following publication M. Sun, B. Huang, Direct Machine Learning Predictions of C3 Pathways. Adv. Energy Mater. 2024, 14, 2400152 is available at https://doi.org/10.1002/aenm.202400152. |
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