Towards atom-level understanding of metal oxide catalysts for the oxygen evolution reaction with machine learning
Source abstract
Abstract
Abstract Green hydrogen production is crucial for a sustainable future, but current catalysts for the oxygen evolution reaction (OER) suffer from slow kinetics, despite many efforts to produce optimal designs, particularly through the calculation of descriptors for activity. In this study, we develop a dataset of density functional theory calculations of bulk and surface perovskite oxides, and adsorption energies of OER intermediates, which includes compositions up to quaternary and facets up to (555). We demonstrate that per-site properties of perovskite oxides such as Bader charge or band center can be tuned through element substitution and faceting, and develop a machine learning model that accurately predicts these properties directly from the local chemical environment. We leverage these per-site properties to identify promising perovskites with high theoretical OER activity. The identified design principles and promising materials provide a roadmap for closing the gap between current artificial catalysts and biological enzymes such as photosystem II.
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Research context
OpenAlex topics: Electrocatalysts for Energy Conversion · Machine Learning in Materials Science · Advanced Photocatalysis Techniques
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References (69)
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1. Hong, W. T. et al. Toward the rational design of non-precious transition metal oxides for oxygen electrocatalysis. Energy Environ. Sci. 8, 1404–1428 (2015).10.1039/C4EE03869J ↗
2. Dau, H. et al. The mechanism of water oxidation: From electrolysis via homogeneous to biological catalysis. ChemCatChem 2, 724–761 (2010).10.1002/cctc.201000126 ↗
3. Cox, N. et al. Eectronic structure of the oxygen- evolving complex in photosystem ii prior to o-o bond formation. Science 345, 804–808 (2014).10.1126/science.1254910 ↗
4. Vogt, C. & Weckhuysen, B. M. The concept of the active site in heterogeneous catalysis. Nat. Rev. Chem. 6, 89–111 (2022).10.1038/s41570-021-00340-y ↗
5. Norskov, J. K., Studt, F., Abild-Pedersen, F. & Bligaard, T. Fundamental concepts in heterogeneous catalysis. John Wiley Sons 1, 32 (2014).
6. Ferri, P. et al. Approaching enzymatic catalysis with zeolites or how to select one reaction mechanism competing with others. Nat. Commun. 14, 2878 (2023).10.1038/s41467-023-38544-z ↗
7. Rao, R. R. et al. Operando identification of site-dependent water oxidation activity on ruthenium dioxide single-crystal surfaces. Nat. Catal. 3, 516–525 (2020).10.1038/s41929-020-0457-6 ↗
8. Halck, N. B., Petrykin, V., Krtil, P. & Rossmeisl, J. Beyond the volcano limitations in electrocatalysis – oxygen evolution reaction. Phys. Chem. Chem. Phys. 16, 13682–13688 (2014).10.1039/C4CP00571F ↗
9. Peng, J. et al. Human-and machine-centred designs of molecules and materials for sustainability and decarbonization. Nat. Rev. Mater. 7, 991–1009 (2022).10.1038/s41578-022-00466-5 ↗
10. Hammer, B. & Norskov, J. K. Why gold is the noblest of all the metals. Nature 376, 238–240 (1995).10.1038/376238a0 ↗
11. Grimaud, A. et al. Double perovskites as a family of highly active catalysts for oxygen evolution in alkaline solution. Nat. Commun. 4, 2439 (2013).10.1038/ncomms3439 ↗
12. 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).10.1002/aenm.201702708 ↗
13. Suntivich, J., May, K. J., Gasteiger, H. A., Goodenough, J. B. & Shao-Horn, Y. A perovskite oxide optimized for oxygen evolution catalysis from molecular orbital principles. Science 334, 1383–1385 (2011).10.1126/science.1212858 ↗
14. Biz, C., Fianchini, M. & Gracia, J. Strongly correlated electrons in catalysis: Focus on quantum exchange. ACS Catal. 11, 14249–14261 (2021).10.1021/acscatal.1c03135 ↗
15. Vennelakanti, V., Nandy, A. & Kulik, H. J. The effect of hartree-fock exchange on scaling relations and reaction energetics for c–h activation catalysts. Top. Catal. 65, 296–311 (2022).10.1007/s11244-021-01482-5 ↗
16. Grimaud, A. et al. Activating lattice oxygen redox reactions in metal oxides to catalyse oxygen evolution. Nat. Chem. 9, 457–465 (2017).10.1038/nchem.2695 ↗
17. Kuznetsov, D. A., Peng, J., Giordano, L., Román-Leshkov, Y. & Shao-Horn, Y. Bismuth substituted strontium cobalt perovskites for catalyzing oxygen evolution. J. Phys. Chem. C. 124, 6562–6570 (2020).10.1021/acs.jpcc.0c01401 ↗
18. Yuan, S. et al. Tunable metal hydroxide–organic frameworks for catalysing oxygen evolution. Nat. Mater. 21, 673–680 (2022).10.1038/s41563-022-01199-0 ↗
19. Calle-Vallejo, F., Inoglu, N. G., Su, H. Y., Kitchin, J. R. & Rossmeisl, J. Number of outer electrons as descriptor for adsorption processes on transition metals and their oxides. Chem. Sci. 4, 1245–1249 (2013).10.1039/c2sc21601a ↗
20. Dickens, C. F. & Norksov, J. K. A theoretical investigation into the role of surface defects for oxygen evolution on ruo2. J. Phys. Chem. C. 121, 18516–18524 (2017).10.1021/acs.jpcc.7b03481 ↗
21. Choubisa, H. et al. Accelerated chemical space search using a quantum-inspired cluster expansion approach. Matter 6, 605–625 (2023).10.1016/j.matt.2022.11.031 ↗
22. Rao, R. R. et al. Towards identifying the active sites on ruo2(110) in catalyzing oxygen evolution. Energy Environ. Sci. 10, 2626–2637 (2017).10.1039/C7EE02307C ↗
23. Dickens, C. et al. An electronic structure descriptor for oxygen reactivity at metal and metal- oxide surfaces. Surf. Sci. 681, 122–129 (2019).10.1016/j.susc.2018.11.019 ↗
24. Hwang, J. et al. Regulating oxygen activity of perovskites to promote nox oxidation and reduction kinetics. Nat. Catal. 4, 663–673 (2021).10.1038/s41929-021-00656-4 ↗
25. 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).10.1038/nchem.2226 ↗
26. Ruck, M. et al. Oxygen reduction reaction: Rapid prediction of mass activity of nanostructured platinum electrocatalysts. J. Phys. Chem. Lett. 9, 4463–4468 (2018).10.1021/acs.jpclett.8b01864 ↗
27. Batchelor, T. A. A. et al. High-entropy alloys as a discovery platform for electrocatalysis. Joule 3, 834–845 (2019).10.1016/j.joule.2018.12.015 ↗
28. 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 (2011).10.1038/s41929-018-0142-1 ↗
29. Fernandez, M., Barron, H. & S. Barnard, A. Artificial neural network analysis of the catalytic efficiency of platinum nanoparticles. RSC Adv. 7, 48962 (2017).10.1039/C7RA06622H ↗
30. Li, Z., Ma, X. & Xin, H. Feature engineering of machine-learning chemisorption models for catalyst design. Catal. Today 280, 232–238 (2017).10.1016/j.cattod.2016.04.013 ↗
31. Axelrod, S. et al. Learning matter: Materials design with machine learning and atomistic simulations. Acc. Mater. Res. 3, 343–357 (2022).10.1021/accountsmr.1c00238 ↗
32. Kitchin, J. R. Machine learning in catalysis. Nat. Catal. 1, 230–232 (2018).10.1038/s41929-018-0056-y ↗
33. Xie, T. & Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120, 145301 (2018).10.1103/PhysRevLett.120.145301 ↗
34. Damewood, J. et al. Representations of materials for machine learning. Annu. Rev. Mater. Res. 53, 399–426 (2023).10.1146/annurev-matsci-080921-085947 ↗
35. Greenman, K. P., Green, W. H. & Gómez-Bombarelli, R. Multi-fidelity prediction of molecular optical peaks with deep learning. Chem. Sci. 13, 1152–1162 (2022).10.1039/D1SC05677H ↗
36. Tran, R. et al. The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysts. ACS Catal. 13, 3066–3084 (2022).10.1021/acscatal.2c05426 ↗
37. Back, S. et al. Convolutional neural network of atomic surface structures to predict binding energies for high-throughput screening of catalysts. J. Phys. Chem. Lett. 10, 4401–4408 (2019).10.1021/acs.jpclett.9b01428 ↗
38. Hwang, J. et al. Perovskites in catalysis and electrocatalysis. Science 358, 751–756 (2017).10.1126/science.aam7092 ↗
39. Rawal, T. B. et al. Interaction of zinc oxide nanoparticles with water: Implications for catalytic activity. ACS Appl. Nano Mater. 2, 4257–4266 (2019).10.1021/acsanm.9b00714 ↗
40. Zhong, W. et al. Electronic spin moment as a catalytic descriptor for Fe single-atom catalysts supported on C2N. J. Am. Chem. Soc. 143, 4405–4413 (2021).10.1021/jacs.1c00889 ↗
41. Phonon database at kyoto university – phonondb documentation. Phonondb.mtl.kyoto-u.ac.jp [Online] (2022).
42. Castelli, I. E. et al. Computational screening of perovskite metal oxides for optimal solar light capture. Energy Environ. Sci. 5, 5814–5819 (2012).10.1039/C1EE02717D ↗
43. Emery, A. A., Saal, J. E., Kirklin, S., Hegde, V. I. & Wolverton, C. High-throughput computational screening of perovskites for thermochemical water splitting applications. Chem. Mater. 28, 5621–5634 (2016).10.1021/acs.chemmater.6b01182 ↗
44. Jain, A. et al. Commentary: The materials project: A materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).10.1063/1.4812323 ↗
45. Kulkarni, A., Siahrostami, S., Patel, A. & Norskov, J. K. Understanding catalytic activity trends in the oxygen reduction reaction. Chem. Rev. 5, 2302–2312 (2018).10.1021/acs.chemrev.7b00488 ↗
46. Gauthier, J. A., Dickens, C. F., Chen, L. D., Doyle, A. D. & Norskov, J. K. Solvation effects for oxygen evolution reaction catalysis on iro2(110). J. Phys. Chem. C. 121, 11455–11463 (2017).10.1021/acs.jpcc.7b02383 ↗
47. Deng, Q. et al. 2d transition metal–tcnq sheets as bifunctional single-atom catalysts. J. Catal. 370, 378–384 (2019).10.1016/j.jcat.2018.12.012 ↗
48. Siegbahn, P. E. M. Water oxidation mechanism in photosystem ii, including oxidations, proton release pathways, o—o bond formation and o2 release. Biochimica et. Biophysica Acta 1827, 1003–1019 (2013).10.1016/j.bbabio.2012.10.006 ↗
49. Schutt, K. T., Unke, O. T. & Gastegger, M. Equivariant message passing for the prediction of tensorial properties and molecular spectra. International Conference on Machine Learning 9377–9388 (2021).
50. Wang, A. Y. T., Kauwe, S. K., Murdock, R. J. & Sparks, T. D. Compositionally restricted attention-based network for materials property predictions. npj Comput. Mater. 7, 77 (2021).10.1038/s41524-021-00545-1 ↗
51. O’Keefe, M. & Breese, N. E. Atom sizes and bond lengths in molecules and crystals. J. Am. Chem. Soc. 113, 3226–3229 (1991).10.1021/ja00009a002 ↗
52. Kutzelnigg, W. & Morgan, J. D. I. Hund’s rules. Z. Phys. D. 36, 197–214 (1996).10.1007/BF01426405 ↗
53. Jacobs, R., Hwang, J., Shao-Horn, Y. & Morgan, D. Assessing correlations of perovskite catalytic performance with electronic structure descriptors. Chem. Mater. 3, 785–797 (2019).10.1021/acs.chemmater.8b03840 ↗
54. Chen, C. & Ong, S. A universal graph deep learning interatomic potential for the periodic table. Nat. Comput. Sci. 2, 718–728 (2022).10.1038/s43588-022-00349-3 ↗
55. Deng, B. et al. Chgnet: Pretrained universal neural network potential for charge-informed atomistic modeling. Nat. Mach. Intell. 5, 1031–1041 (2023).10.1038/s42256-023-00716-3 ↗
56. Lee, Y. L. et al. Prediction of solid oxide fuel cell cathode activity with first-principles descriptors. Energy Environ. Sci. 4, 3966 (2011).10.1039/c1ee02032c ↗
57. Craig, M. J., Kleuker, F., Badjdich, M. & Garcia-Melchor, M. Fefos: A method to derive oxide formation energies from oxidation states. Catal. Sci. Technol. 13, 3427–3435 (2023).10.1039/D3CY00107E ↗
58. 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).10.1021/acs.jpcc.5b10071 ↗
59. Sun, S., Zhou, X., Cong, B., Hong, W. & Chen, G. Tailoring the d-band centers endows (nixfe1–x)2p nanosheets with efficient oxygen evolution catalysis. ACS Catal. 10, 9086–9097 (2020).10.1021/acscatal.0c01273 ↗
60. Man, I. C. et al. Universality in oxygen evolution electrocatalysis on oxide surfaces. ChemCatChem 3, 1159–1165 (2011).10.1002/cctc.201000397 ↗
61. Rossmeisl, J., Dimitrievski, K., Siegbahn, P. & Norskov, J. K. Comparing electrochemical and biological water splitting. J. Phys. Chem. Lett. C. 111, 18821–18823 (2007).10.1021/jp077210j ↗
62. Pedersen, J. K. et al. Bayesian optimization of high-entropy alloy compositions for electrocatalytic oxygen reduction. Angew. Chem. 1333, 24346–24354 (2021).10.1002/ange.202108116 ↗
63. Svane, K. & Rossmeisl, J. High-entropy oxides for the oxygen evolution reaction. ChemRxiv. Cambridge Open Engage 1 (2022).10.26434/chemrxiv-2022-7wwnh ↗
64. Nguyen, T. X., Liao, Y. C., Lin, C. C., Su, Y. H. & Ting, J. M. Advanced high entropy perovskite oxide electrocatalyst for oxygen evolution reaction. Adv. Funct. Mater. 31, 2101632 (2021).10.1002/adfm.202101632 ↗
65. Peng, J., Damewood, J. & Gómez-Bombarelli, R. Data-driven, physics-informed descriptors of cation ordering in multicomponent oxides. arXiv (2023).10.1016/j.xcrp.2024.101942 ↗
66. Clark, S. & Hayes, P. SigOpt Web page. https://sigopt.com (2019).
67. Mueller, D. N., Machala, M. L., Bluhm, H. & Chueh, W. C. Redox activity of surface oxygen anions in oxygen-deficient perovskite oxides during electrochemical reactions. Nat. Commun. 6, 6097 (2015).10.1038/ncomms7097 ↗
68. Chase, M. Nist-janaf thermochemical tables. Am. Inst. Phys. 4, 1261–1359 (1998).
69. Lohmiller, T. et al. Structure, ligands and substrate coordination of the oxygen-evolving complex of photosystem ii in the s2 state: a combined epr and dft study. Phys. Chem. Chem. Phys. 16, 11877–11892 (2014).10.1039/c3cp55017f ↗