Article | Published:

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

Nature Catalysisvolume 1pages696703 (2018) | Download Citation


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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Data availability

The code and data used to produce the figures in this article are available in the GASpy manuscript repository at A snapshot of our adsorption energy data are included with this article in JSON format. A ‘’ 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

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    World Energy Outlook 2017 Technical Report (International Energy Agency, 2017);

  2. 2.

    Annual Energy Outlook 2017 with Projections to 2050 Technical Report (US Energy Information Administration, 2017);

  3. 3.

    Mackay, D. J. C. Sustainable Energy—without the Hot Air Vol. 2 (UIT Cambridge Ltd, Cambridge, 2009).

  4. 4.

    Edenhofer, O., Madruga, R. P. & Sokona, Y. Renewable Energy Sources and Climate Change Mitigation (Cambridge Univ. Press, 2012).

  5. 5.

    Rockström, J. et al. A safe operating space for humanity. Nature 461, 472–475 (2009).

  6. 6.

    IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2015).

  7. 7.

    Lewis, N. S. & Nocera, D. G. Powering the planet: chemical challenges in solar energy utilization. Proc. Natl Acad. Sci. USA 104, 15729–15735 (2007).

  8. 8.

    Seh, Z. W. et al. Combining theory and experiment in electrocatalysis: insights into materials design. Science 355, eaad4998 (2017).

  9. 9.

    Montoya, J. H. et al. Materials for solar fuels and chemicals. Nat. Mater. 16, 70–81 (2016).

  10. 10.

    Liu, X. et al. Understanding trends in electrochemical carbon dioxide reduction rates. Nat. Commun. 8, 15438 (2017).

  11. 11.

    Nørskov, J. K., Studt, F., Abild-Pedersen, F. & Bligaard, T. Fundamental Concepts in Heterogeneous Catalysis (John Wiley & Sons, Inc., Hoboken, 2015).

  12. 12.

    Greeley, J., Jaramillo, T. F., Bonde, J., Chorkendorff, I. & Nørskov, J. K. Computational high-throughput screening of electrocatalytic materials for hydrogen evolution. Nat. Mater. 5, 909–913 (2006).

  13. 13.

    Hansen, H. A., Shi, C., Lausche, A. C., Peterson, A. A. & Nørskov, J. K. Bifunctional alloys for the electroreduction of CO2 and CO. Phys. Chem. Chem. Phys. 18, 9194–9201 (2016).

  14. 14.

    Li, Z., Wang, S., Chin, W. S., Achenie, L. E. & Xin, H. High-throughput screening of bimetallic catalysts enabled by machine learning. J. Mater. Chem. A 5, 24131–24138 (2017).

  15. 15.

    Hummelshøj, J. S., Abild-Pedersen, F., Studt, F., Bligaard, T. & Nørskov, J. K. CatApp: a web application for surface chemistry and heterogeneous catalysis. Angew. Chem. Int. Ed. 51, 272–274 (2012).

  16. 16.

    Scheffler, M. & Draxl, C. The NOMAD Repository (Computer Center of the Max-Planck Society, Garching, 2014).

  17. 17.

    Ong, S. P. et al. Python Materials Genomics (pymatgen): a robust, open-source python library for materials analysis. Comp. Mater. Sci. 68, 314–319 (2013).

  18. 18.

    Montoya, J. H. & Persson, K. A. A high-throughput framework for determining adsorption energies on solid surfaces. Comp. Mater. 3, 14 (2017).

  19. 19.

    Jain, A. et al. FireWorks: a dynamic workflow system designed for high-throughput applications. Concurr. Comp. Pract. E. 22, 685–701 (2010).

  20. 20.

    Meredig, B. et al. Combinatorial screening for new materials in unconstrained composition space with machine learning. Phys. Rev. B 89, 1–7 (2014).

  21. 21.

    Ward, L. et al. Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations. Phys. Rev. B 96, 1–12 (2017).

  22. 22.

    Ulissi, Z. W. et al. Machine-learning methods enable exhaustive searches for active bimetallic facets and reveal active site motifs for CO2 reduction. ACS Catal. 7, 6600–6608 (2017).

  23. 23.

    Peterson, A. A. Acceleration of saddle-point searches with machine learning. J. Chem. Phys. 145, 074106 (2016).

  24. 24.

    Boes, J. R. & Kitchin, J. R. Modeling segregation on AuPd(111) surfaces with density functional theory and Monte Carlo simulations. J. Phys. Chem. C 121, 3479–3487 (2017).

  25. 25.

    Han, Z. H. & Zhang, K. S. in Real-World Applications of Genetic Algorithms (ed. Roeva, O.) Ch. 17 (InTech, London, 2012).

  26. 26.

    Settles, B. Active Learning (Williston, Morgan & Claypool, 2012).

  27. 27.

    Gómez-Bombarelli, R. et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat. Mater. 15, 1120–1127 (2016).

  28. 28.

    Warmuth, M. K. et al. Active learning with support vector machines in the drug discovery process. J. Chem. Inf. Comput. Sci. 43, 667–673 (2003).

  29. 29.

    Gubaev, K., Podryabinkin, E. V. & Shapeev, A. V. Machine learning of molecular properties: Locality and active learning. J. Chem. Phys. 148, 1–9 (2018).

  30. 30.

    Jain, A. et al. The materials project: A materials genome approach to accelerating materials innovation. APL Mater. 1, 1–11 (2013).

  31. 31.

    Lukasz, M. Mendeleev—a Python resource for properties of chemical elements, ions and isotopes (2014);

  32. 32.

    Davie, S. J., Di Pasquale, N. & Popelier, P. L. Kriging atomic properties with a variable number of inputs. J. Chem. Phys 145, 1–11 (2016).

  33. 33.

    Calle-Vallejo, F., Loffreda, D., Koper, M. T. M. & Sautet, P. Introducing structural sensitivity into adsorption-energy scaling relations by means of coordination numbers. Nat. Chem. 7, 403–410 (2015).

  34. 34.

    Zhang, Y. & Ling, C. A strategy to apply machine learning to small datasets in materials science. Comp. Mater. 25, 28–33 (2018).

  35. 35.

    Olson, R. S. et al. in Applications of Evolutionary Computation (eds Squillero, G. & Burelli, P.) 123–137 (Lecture Notes in Computer Science, Vol. 9597, Springer International Publishing, Porto, 2016).

  36. 36.

    Hyndman, R. J. & Athanasopoulos, G. Forecasting: Principles and Practice (2014);

  37. 37.

    Morimoto, M. et al. Electrodeposited Cu–Sn alloy for electrochemical CO2 reduction to CO/HCOO. Electrocatalysis 9, 323–332 (2018).

  38. 38.

    Torelli, D. A. et al. Nickel–gallium-catalyzed electrochemical reduction of CO2 to highly reduced products at low overpotentials. ACS Catal. 6, 2100–2104 (2016).

  39. 39.

    Kortlever, R. et al. Palladium–gold catalyst for the electrochemical reduction of CO2 to C2–C5 hydrocarbons. Chem. Commun. 52, 10229–10232 (2016).

  40. 40.

    Maaten, L. V. D. Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 1–21 (2014).

  41. 41.

    Cherepanov, P. V., Ashokkumar, M. & Andreeva, D. V. Ultrasound assisted formation of Al–Ni electrocatalyst for hydrogen evolution. Ultrason. Sonochem. 23, 142–147 (2015).

  42. 42.

    Yamauchi, M., Abe, R., Tsukuda, T., Kato, K. & Takata, M. Highly selective ammonia synthesis from nitrate with photocatalytically generated hydrogen on CuPd/TiO2. J. Am. Chem. Soc. 133, 1150–1152 (2011).

  43. 43.

    Liao, H. et al. A multisite strategy for enhancing the hydrogen evolution reaction on a nano-Pd surface in alkaline media. Adv. Energ. Mater. 7, 1–7 (2017).

  44. 44.

    Nørskov, J. K. et al. Trends in the exchange current for hydrogen evolution. J. Electrochem. Soc. 152, J23 (2005).

  45. 45.

    Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. Rev. B 47, 558–561 (1993).

  46. 46.

    Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251–14269 (1994).

  47. 47.

    Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comp. Mater. Sci. 6, 15–50 (1996).

  48. 48.

    Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169–11186 (1996).

  49. 49.

    Hjorth Larsen, A. et al. The atomic simulation environment—a Python library for working with atoms. J. Phys. Condens. Mat. 29, 273002 (2017).

  50. 50.

    Hammer, B., Hansen, L. B. & Nørskov, J. Improved adsorption energetics within density-functional theory using revised Perdew–Burke–Ernzerhof functionals. Phys. Rev. B 59, 7413–7421 (1999).

  51. 51.

    Bernhardsson, E., Freider, E. & Rouhani, A. Luigi, a Python package that builds complex pipelines of batch jobs (bithub, 2012);

  52. 52.

    Mathew, K. et al. Atomate: a high-level interface to generate, execute, and analyze computational materials science workflows. Comp. Mater. Sci. 139, 140–152 (2017).

Download references


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.

Author information


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

    • Kevin Tran
    •  & Zachary W. Ulissi


  1. Search for Kevin Tran in:

  2. Search for Zachary W. Ulissi in:


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.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Zachary W. Ulissi.

Supplementary information

  1. Supplementary Information

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

About this article

Publication history





Further reading Further reading