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Nature Computational Science volume 2, pages 477–478 (2022)
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A machine learning method is developed and used to predict the adsorption configurations and energies of complex molecules at the surfaces of transition metals and alloys. This method will be useful for investigating complex reaction networks at complex catalyst materials to understand and improve the performance of heterogeneous catalysts.
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Nørskov, J. K., Abild-Pedersen, F., Studt, F. & Bligaard, T. Density functional theory in surface chemistry and catalysis. Proc. Natl Acad. Sci. USA 108, 937–943 (2011). A review article that presents the role of QM calculations in catalysis research.
Article Google Scholar
Kitchin, J. R. Machine learning in catalysis. Nat. Catal. 1, 230–232 (2018). A review article that presents the role of machine learning methods in catalysis research.
Article Google Scholar
Togninalli, M., Ghisu, E., Llinares-López, F., Rieck, B. & Borgwardt, K. Wasserstein Weisfeiler-Lehman graph kernels. Adv. Neural Inf. Process. Syst. 578, 6439–6449 (2019). This paper proposes the Wasserstein Weisfeiler–Lehman graph kernel used in our work.
Google Scholar
Chanussot, L. et al. Open Catalyst 2020 (OC20) dataset and community challenges. ACS Catal. 11, 6059–6072 (2021). This paper presents open datasets and challenges for the machine learning community in catalysis research.
Xu, W., Andersen, M. & Reuter, K. Data-driven descriptor engineering and refined scaling relations for predicting transition metal oxide reactivity. ACS Catal 11, 734–742 (2020). This paper reports a machine learning approach for predicting the adsorption energies of atoms and small molecules at metal oxides.
Article Google Scholar
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This is a summary of: Xu, W. et al. Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation. Nat. Comput. Sci. https://doi.org/10.1038/s43588-022-00280-7 (2022).
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Machine learning reveals how complex molecules bind to catalyst surfaces. Nat Comput Sci 2, 477–478 (2022). https://doi.org/10.1038/s43588-022-00287-0
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Published: 02 August 2022
Issue Date: August 2022
DOI: https://doi.org/10.1038/s43588-022-00287-0
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