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Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation

Nature Computational Science volume 2, pages 443–450 (2022)Cite this article

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A preprint version of the article is available at arXiv.

Abstract

Computational screening in heterogeneous catalysis relies increasingly on machine learning models for predicting key input parameters due to the high cost of computing these directly using first-principles methods. This becomes especially relevant when considering complex materials spaces such as alloys, or complex reaction mechanisms with adsorbates that may exhibit bi- or higher-dentate adsorption motifs. Here we present a data-efficient approach to the prediction of binding motifs and associated adsorption enthalpies of complex adsorbates at transition metals and their alloys based on a customized Wasserstein Weisfeiler–Lehman graph kernel and Gaussian process regression. The model shows good predictive performance, not only for the elemental transition metals on which it was trained, but also for an alloy based on these transition metals. Furthermore, incorporation of minimal new training data allows for predicting an out-of-domain transition metal. We believe the model may be useful in active learning approaches, for which we present an ensemble uncertainty estimation approach.

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Fig. 1: Schematic illustration of the WWL-GPR model.
Fig. 2: Parity plot of DFT-calculated versus machine learning-predicted adsorption enthalpies using SISSO, RBF-GPR and WWL-GPR.
Fig. 3: Parity plot of DFT-calculated versus machine learning-predicted adsorption enthalpies using RBF-GPR, WWL-GPR and XGBoost.
Fig. 4: Kernel principal component analysis.
Fig. 5: Estimated uncertainties versus absolute prediction errors for the single and ensemble models.

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Data availability

The DFT-calculated adsorption energies and relaxed coordinates of the simple and complex adsorbates databases as well as all calculated features are available at https://github.com/Wenbintum/WWL-GPR and Zenodo48. Source Data are provided with this paper.

Code availability

The source code of WWL-GPR is publicly available on GitHub at https://github.com/Wenbintum/WWL-GPR and Zenodo48. We provide predefined tasks for tutorial purposes and for reproducing the results presented in this work. The RBF-GPR is implemented with Scikit-learn55, which is available at https://scikit-learn.org. The SISSO code18 is available at https://github.com/rouyang2017/SISSO, and the XGBoost code20 is available at https://github.com/dmlc/xgboost.

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Acknowledgements

The authors gratefully acknowledge support from the Max Planck Computing and Data Facility (MPCDF) and the Jülich Supercomputing Centre (www.fz-juelich.de/ias/jsc). W.X. is grateful for support through the China Scholarship Council (CSC). M.A. acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie (grant agreement no. 754513), the Aarhus University Research Foundation, the Danish National Research Foundation through the Center of Excellence ’InterCat’ (grant agreement no. DNRF150) and VILLUM FONDEN (grant no. 37381).

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Authors and Affiliations

  1. Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Garching, Germany

    Wenbin Xu

  2. Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany

    Wenbin Xu & Karsten Reuter

  3. Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark

    Mie Andersen

  4. Department of Physics and Astronomy—Center for Interstellar Catalysis, Aarhus University, Aarhus, Denmark

    Mie Andersen

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Contributions

W.X. performed the DFT calculations, workflow and machine learning methods development. K.R. and M.A. conceived and supervised the project. All authors contributed to analyzing the data and writing the manuscript.

Corresponding author

Correspondence to Mie Andersen.

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Nature Computational Science thanks Gyoung Na, Hongliang Xin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Handling editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.

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Supplementary Sections 1–4, Figs. 1–9 and Tables 1–12.

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Source data

Source Data Fig. 2

DFT-calculated versus machine learning-predicted (SISSO, RBF-GPR and WWL-GPR) adsorption enthalpies for the simple adsorbates database.

Source Data Fig. 3

DFT-calculated versus machine learning-predicted (RBF-GPR, WWL-GPR and XGBoost) adsorption enthalpies for the complex adsorbates database.

Source Data Fig. 4

Principal components 1 and 2 from kernel principal component analysis for the complex adsorbates database and the WWL-GPR model (all metals and Rh metal only).

Source Data Fig. 5

DFT-calculated adsorption enthalpies, machine learning-predicted adsorption enthalpies (WWL-GPR) and predicted uncertainties (single GPR model and ensemble model).

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Xu, W., Reuter, K. & Andersen, M. Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation. Nat Comput Sci 2, 443–450 (2022). https://doi.org/10.1038/s43588-022-00280-7

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