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Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution

Nature Catalysis volume 1, pages 696–703 (2018)Cite this article

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Abstract

The electrochemical reduction of CO2 and H2 evolution from water can be used to store renewable energy that is produced intermittently. Scale-up of these reactions requires the discovery of effective electrocatalysts, but the electrocatalyst search space is too large to explore exhaustively. Here we present a theoretical, fully automated screening method that uses a combination of machine learning and optimization to guide density functional theory calculations, which are then used to predict electrocatalyst performance. We demonstrate the feasibility of this method by screening various alloys of 31 different elements, and thereby perform a screening that encompasses 50% of the d-block elements and 33% of the p-block elements. This method has thus far identified 131 candidate surfaces across 54 alloys for CO2 reduction and 258 surfaces across 102 alloys for H2 evolution. We use qualitative analyses to prioritize the top candidates for experimental validation.

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Fig. 1: Workflow for automating theoretical materials discovery.
Fig. 2: Fingerprint of coordination site.
Fig. 3: Identification of surfaces with near-optimal ∆ECO values for CO2RR.
Fig. 4: CO2 reduction activity map for bimetallics.
Fig. 5: Active site motif analysis.
Fig. 6: Analysis of results for the HER performance.

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Data availability

The code and data used to produce the figures in this article are available in the GASpy manuscript repository at https://github.com/ulissigroup/GASpy_manuscript. A snapshot of our adsorption energy data are included with this article in JSON format. A ‘mongo.py’ and a ‘how_to_read_gasdb_json.ipynb’ Jupyter notebook are also included to illustrate how to convert the JSON data into atoms objects as per the ASE49. Up-to-date versions of the JSON-formatted data are also available from the corresponding author on reasonable request. An up-to-date visualization of the data can also be viewed at http://sm1.cheme.cmu.edu/volcano.

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Acknowledgements

This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy under contract no. DE-AC02-05CH11231. We thank K. Chan for helpful discussions about descriptor targets, as well as P. de Luna and E. T. Sargent for helpful discussions about analysis.

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  1. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Kevin Tran & Zachary W. Ulissi

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  1. Kevin Tran
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Contributions

K.T. and Z.W.U. contributed to the scientific workflow software and DFT calculations. K.T. and Z.W.U. made the regression models and analysis. K.T. performed the clustering analysis. K.T. and Z.W.U. wrote the manuscript. Z.W.U. conceived the idea.

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Correspondence to Zachary W. Ulissi.

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Supplementary Figures 1–7, Supplementary Tables 1 & 2, Supplementary Notes 1–3, Supplementary Methods & Supplementary References

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Tran, K., Ulissi, Z.W. Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution. Nat Catal 1, 696–703 (2018). https://doi.org/10.1038/s41929-018-0142-1

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