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. 2023 May 25;14:3004. doi: 10.1038/s41467-023-38758-1

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a Schematic representation of the graph convolution neural network model to predict the adsorption energy. b Representation of bond embedding. Each bond is embedded into a bond vector by one-hot encoding of the bond type. c Optimization of atom embedding features. Costs are compared as a function of various feature combinations. Blue points/line denote the minimum value of each feature combination. Here, GR, AR, EA, and PL indicate the group number, atomic radius, electron affinity, and polarizability, respectively.