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A combined ionic Lewis acid descriptor and machine-learning approach to prediction of efficient oxygen reduction electrodes for ceramic fuel cells

Abstract

Improved, highly active cathode materials are needed to promote the commercialization of ceramic fuel cell technology. However, the conventional trial-and-error process of material design, characterization and testing can make for a long and complex research cycle. Here we demonstrate an experimentally validated machine-learning-driven approach to accelerate the discovery of efficient oxygen reduction electrodes, where the ionic Lewis acid strength (ISA) is introduced as an effective physical descriptor for the oxygen reduction reaction activity of perovskite oxides. Four oxides, screened from 6,871 distinct perovskite compositions, are successfully synthesized and confirmed to have superior activity metrics. Experimental characterization reveals that decreased A-site and increased B-site ISAs in perovskite oxides considerably improve the surface exchange kinetics. Theoretical calculations indicate such improved activity is mainly attributed to the shift of electron pairs caused by polarization distribution of ISAs at sites A and B, which greatly reduces oxygen vacancy formation energy and migration barrier.

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Fig. 1: The overall workflow diagram.
Fig. 2: Model evaluation and descriptor importance degree analysis.
Fig. 3: Structure and electrochemical performance of the synthesized perovskite oxide sample.
Fig. 4: Symmetrical cell stability and single cell performance based on SCCN cathode.
Fig. 5: Morphology of BSCCFM.
Fig. 6: Oxygen-transfer-related characterization.
Fig. 7: DFT calculation of electronic structure evolution.

Data availability

All relevant data are included in the paper and its Supplementary Information. Source Data are provided with this paper.

Code availability

The Python script for machine-learning model training and material screening are available at https://github.com/nlpcui/MLORR.

References

  1. Shao, Z. & Haile, S. M. A high-performance cathode for the next generation of solid-oxide fuel cells. Nature 431, 170–173 (2004).

    Article  Google Scholar 

  2. Wachsman, E. D. & Lee, K. T. Lowering the temperature of solid oxide fuel cells. Science 334, 935–939 (2011).

    Article  Google Scholar 

  3. Duan, C. et al. Highly efficient reversible protonic ceramic electrochemical cells for power generation and fuel production. Nat. Energy 4, 230–240 (2019).

    Article  Google Scholar 

  4. Ni, M. & Shao, Z. Fuel cells that operate at 300° to 500 °C. Science 369, 138–139 (2020).

    Article  Google Scholar 

  5. Xie, H. et al. Cu-modified Ni foams as three-dimensional outer anodes for high-performance hybrid direct coal fuel cells. Chem. Eng. J. 410, 128239 (2021).

    Article  Google Scholar 

  6. Wachsman, E. D., Marlowe, C. A. & Lee, K. T. Role of solid oxide fuel cells in a balanced energy strategy. Energy Environ. Sci. 5, 5498–5509 (2012).

    Article  Google Scholar 

  7. Duan, C. et al. Readily processed protonic ceramic fuel cells with high performance at low temperatures. Science 349, 1321–1326 (2015).

    Article  Google Scholar 

  8. Song, Y. et al. A cobalt‐free multi‐phase nanocomposite as near‐ideal cathode of intermediate‐temperature solid oxide fuel cells developed by smart self‐assembly. Adv. Mater. 32, 1906979 (2020).

    Article  Google Scholar 

  9. Jun, A., Kim, J., Shin, J. & Kim, G. Perovskite as a cathode material: a review of its role in solid‐oxide fuel cell technology. ChemElectroChem 3, 511–530 (2016).

    Article  Google Scholar 

  10. Bello, I. T., Zhai, S., He, Q., Xu, Q. & Ni, M. Scientometric review of advancements in the development of high-performance cathode for low and intermediate temperature solid oxide fuel cells: three decades in retrospect. Int. J. Hydrog. Energy 46, 26518–26536 (2021).

    Article  Google Scholar 

  11. Sunarso, J., Hashim, S. S., Zhu, N. & Zhou, W. Perovskite oxides applications in high temperature oxygen separation, solid oxide fuel cell and membrane reactor: a review. Prog. Energy Combust. Sci. 61, 57–77 (2017).

    Article  Google Scholar 

  12. Suntivich, J. et al. Design principles for oxygen-reduction activity on perovskite oxide catalysts for fuel cells and metal–air batteries. Nat. Chem. 3, 546–550 (2011).

    Article  Google Scholar 

  13. Schmidt, J., Marques, M. R., Botti, S. & Marques, M. A. Recent advances and applications of machine learning in solid-state materials science. npj Comput. Mater. 5, 1–36 (2019).

    Article  Google Scholar 

  14. Lee, Y.-L., Kleis, J., Rossmeisl, J., Shao-Horn, Y. & Morgan, D. Prediction of solid oxide fuel cell cathode activity with first-principles descriptors. Energy Environ. Sci. 4, 3966–3970 (2011).

    Article  Google Scholar 

  15. Calle-Vallejo, F., Díaz-Morales, O. A., Kolb, M. J. & Koper, M. T. Why is bulk thermochemistry a good descriptor for the electrocatalytic activity of transition metal oxides? ACS Catal. 5, 869–873 (2015).

    Article  Google Scholar 

  16. Hong, W. T. et al. Charge-transfer-energy-dependent oxygen evolution reaction mechanisms for perovskite oxides. Energy Environ. Sci. 10, 2190–2200 (2017).

    Article  Google Scholar 

  17. Jacobs, R., Mayeshiba, T., Booske, J. & Morgan, D. Material discovery and design principles for stable, high activity perovskite cathodes for solid oxide fuel cells. Adv. Energy Mater. 8, 1702708 (2018).

    Article  Google Scholar 

  18. Guan, D. et al. Screening highly active perovskites for hydrogen-evolving reaction via unifying ionic electronegativity descriptor. Nat. Commun. 10, 1–8 (2019).

    Article  Google Scholar 

  19. Calle-Vallejo, F. et al. Number of outer electrons as descriptor for adsorption processes on transition metals and their oxides. Chem. Sci. 4, 1245–1249 (2013).

    Article  Google Scholar 

  20. Stoerzinger, K. A., Risch, M., Han, B. & Shao-Horn, Y. Recent insights into manganese oxides in catalyzing oxygen reduction kinetics. ACS Catal. 5, 6021–6031 (2015).

    Article  Google Scholar 

  21. Hu, S. & Li, W.-X. Sabatier principle of metal-support interaction for design of ultrastable metal nanocatalysts. Science 374, eabi9828 (2021).

    Article  Google Scholar 

  22. Weng, B. et al. Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts. Nat. Commun. 11, 1–8 (2020).

    Article  Google Scholar 

  23. Shannon, R. T. & Prewitt, C. T. Effective ionic radii in oxides and fluorides. Acta Crystallogr. B 25, 925–946 (1969).

    Article  Google Scholar 

  24. Lide, D. R. CRC Handbook of Chemistry and Physics Vol. 85 (CRC, 2004).

  25. Wei, Y. et al. Activation strategies of perovskite-type structure for applications in oxygen-related electrocatalysts. Small Methods 5, 2100012 (2021).

    Article  Google Scholar 

  26. Zhou, W. et al. Barium-and strontium-enriched (Ba0.5Sr0.5)1+xCo0.8Fe0.2O3−δ oxides as high-performance cathodes for intermediate-temperature solid-oxide fuel cells. Acta Mater. 56, 2687–2698 (2008).

    Article  Google Scholar 

  27. Ciucci, F. & Chen, C. J. E. A. Analysis of electrochemical impedance spectroscopy data using the distribution of relaxation times: a Bayesian and hierarchical Bayesian approach. Electrochim. Acta 167, 439–454 (2015).

    Article  Google Scholar 

  28. Song, Y. et al. Nanocomposites: a new opportunity for developing highly active and durable bifunctional air electrodes for reversible protonic ceramic cells.Adv. Energy Mater. 11, 2101899 (2021).

    Article  Google Scholar 

  29. Jiang, Y. et al. Combination of hybrid CVD and cation exchange for upscaling Cs‐substituted mixed cation perovskite solar cells with high efficiency and stability. Adv. Funct. Mater. 28, 1703835 (2018).

    Article  Google Scholar 

  30. Yang, X. et al. Improving stability and electrochemical performance of Ba0.5Sr0.5Co0.2Fe0.8O3–δ electrode for symmetrical solid oxide fuel cells by Mo doping. J. Alloy. Compd. 831, 154711 (2020).

    Article  Google Scholar 

  31. Shen, L., Du, Z., Zhang, Y., Dong, X. & Zhao, H. Medium-entropy perovskites Sr(FeαTiβCoγMnζ)O3–δ as promising cathodes for intermediate temperature solid oxide fuel cell. Appl. Catal. B 295, 120264 (2021).

    Article  Google Scholar 

  32. Zhang, Y. et al. Thermal-expansion offset for high-performance fuel cell cathodes. Nature 591, 246–251 (2021).

    Article  Google Scholar 

  33. Li, X. et al. Ultrafast room-temperature synthesis of self-supported NiFe-layered double hydroxide as large-current-density oxygen evolution electrocatalyst. Small 18, 2104354 (2022).

    Article  Google Scholar 

  34. Liang, M. et al. A new durable surface nanoparticles-modified perovskite cathode for protonic ceramic fuel cells from selective cation exsolution under oxidizing atmosphere. Adv. Mater. 34, 2106379 (2022).

    Article  Google Scholar 

  35. Ramos, A. E., Maiti, D., Daza, Y. A., Kuhn, J. N. & Bhethanabotla, V. R. Co, Fe, and Mn in La-perovskite oxides for low temperature thermochemical CO2 conversion. Catal. Today 338, 52–59 (2019).

    Article  Google Scholar 

  36. Sun, Y.-R. et al. Lattice contraction tailoring in perovskite oxides towards improvement of oxygen electrode catalytic activity. Chem. Eng. J. 421, 129698 (2021).

    Article  Google Scholar 

  37. Hong, W. T., Welsch, R. E. & Shao-Horn, Y. Descriptors of oxygen-evolution activity for oxides: a statistical evaluation. J. Phys. Chem. C 120, 78–86 (2016).

    Article  Google Scholar 

  38. Suntivich, J., May, K. J., Gasteiger, H. A., Goodenough, J. B. & Shao-Horn, Y. J. S. A perovskite oxide optimized for oxygen evolution catalysis from molecular orbital principles. Science 334, 1383–1385 (2011).

    Article  Google Scholar 

  39. Liang, M. et al. Nickel-doped BaCo0.4Fe0.4Zr0.1Y0.1O3-δ as a new high-performance cathode for both oxygen-ion and proton conducting fuel cells. Chem. Eng. J. 420, 127717 (2021).

    Article  Google Scholar 

  40. Guan, D. et al. Exceptionally robust face‐sharing motifs enable efficient and durable water oxidation. Advanced Materials 33, 2103392 (2021).

    Article  Google Scholar 

  41. Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B 59, 1758 (1999).

    Article  Google Scholar 

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

    Article  Google Scholar 

  43. Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865 (1996).

    Article  Google Scholar 

  44. Blöchl, P. E. Projector augmented-wave method. Phys. Rev. B 50, 17953 (1994).

    Article  Google Scholar 

  45. Grimme, S., Antony, J., Ehrlich, S. & Krieg, H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H–Pu. J. Chem. Phys. 132, 154104 (2010).

    Article  Google Scholar 

  46. Henkelman, G., Uberuaga, B. P. & Jónsson, H. A climbing image nudged elastic band method for finding saddle points and minimum energy paths. J. Chem. Phys. 113, 9901–9904 (2000).

    Article  Google Scholar 

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Acknowledgements

This work is supported by National Natural Science Foundation of China (grant no. 51827901, H.X.), Project of Strategic Importance Program of The Hong Kong Polytechnic University (grant no P0035168, M.N.), Sichuan Science and Technology Department (grant no. 2020YFH0012, H.X.), Program for Guangdong Introducing Innovative and Entrepreneurial Teams (grant no. 2019ZT08G315, H.X.), as well as the Natural Science Foundation of Guangdong Province (grant no. 2020A1515010550, H.X.).

Author information

Authors and Affiliations

Authors

Contributions

S.Z., H.X., M.N. and Z.S. conceived the idea and designed the experiments. P.C. provided machine-learning codes. S.Z., S.Y.Z. and B.C. carried out the sample synthesis, characterization and measurements. Y.S, D.G. and J.W. participated in the discussion of the experimental results. S.Z., H.X., M.N. and Z.S. co-wrote and revised the manuscript.

Corresponding authors

Correspondence to Heping Xie, Zongping Shao or Meng Ni.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Energy thanks Yueh-Lin Lee, Steven McIntosh and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Methods, Notes 1–5, Figs. 1–24 and Tables 1–8.

Supplementary Data 1

The nine ionic descriptor values for each perovskite in the dataset.

Supplementary Data 2

The ASR predicted values for 6,871 distinct perovskite oxide compositions.

Source data

Source Data Fig. 2

Model evaluation and descriptor importance degree analysis.

Source Data Fig. 3

Structure and electrochemical performance of the synthesized perovskite oxide sample.

Source Data Fig. 4

Symmetrical cell stability and single cell performance based on SCCN cathode.

Source Data Fig. 6

Oxygen-transfer related characterization.

Source Data Fig. 7

DFT calculation of electronic structure evolution.

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Zhai, S., Xie, H., Cui, P. et al. A combined ionic Lewis acid descriptor and machine-learning approach to prediction of efficient oxygen reduction electrodes for ceramic fuel cells. Nat Energy 7, 866–875 (2022). https://doi.org/10.1038/s41560-022-01098-3

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