Understanding electrified interfaces is crucial to enabling a multitude of applications, including photo(electrocatalysis), supercapacitors, pseudocapacitors and batteries. However, reaching an atomistic understanding of electrified interfaces remains challenging and will require the combination and development of refined computations and experiments.
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References
Seh, Z. W. et al. Combining theory and experiment in electrocatalysis: insights into materials design. Science 355, eaad4998 (2017).
Abidi, N., Lim, K. R. G., Seh, Z. W. & Steinmann, S. N. Atomistic modeling of electrocatalysis: are we there yet? WIREs Comput. Mol. Sci. https://doi.org/10.1002/wcms.1499 (2020).
Back, S., Tran, K. & Ulissi, Z. W. Discovery of acid-stable oxygen evolution catalysts: high-throughput computational screening of equimolar bimetallic oxides. ACS Appl. Mater. Interfaces 12, 38256–38265 (2020).
Toyao, T. et al. Machine learning for catalysis informatics: recent applications and prospects. ACS Catal. 10, 2260–2297 (2020).
Bartók, A. P. et al. Machine learning unifies the modeling of materials and molecules. Sci. Adv. 3, e1701816 (2017).
Batchelor, T. A. A. et al. High-entropy alloys as a discovery platform for electrocatalysis. Joule 3, 834–845 (2019).
Chen, Y., Huang, Y., Cheng, T. & Goddard, W. A. Identifying active sites for CO2 reduction on dealloyed gold surfaces by combining machine learning with multiscale simulations. J. Am. Chem. Soc. 141, 11651–11657 (2019).
Xie, X., Persson, K. A. & Small, D. W. Incorporating electronic information into machine learning potential energy surfaces via approaching the ground-state electronic energy as a function of atom-based electronic populations. J. Chem. Theory Comput. 16, 4256–4270 (2020).
Doblhoff-Dier, K., Meyer, J., Hoggan, P. E. & Kroes, G.-J. Quantum Monte Carlo calculations on a benchmark molecule–metal surface reaction: H2 + Cu(111). J. Chem. Theory Comput. 13, 3208–3219 (2017).
Handoko, A. D., Wei, F., Jenndy, Yeo, B. S. & Seh, Z. W. Understanding heterogeneous electrocatalytic carbon dioxide reduction through operando techniques. Nat. Catal. 1, 922–934 (2018).
Acknowledgements
This work is supported by the Singapore National Research Foundation (NRF-NRF2017-04) and by Région Auvergne Rhône-Alpes through the project Pack Ambition Recherche 2018 MoSHi.
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Steinmann, S.N., Seh, Z.W. Understanding electrified interfaces. Nat Rev Mater 6, 289–291 (2021). https://doi.org/10.1038/s41578-021-00303-1
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DOI: https://doi.org/10.1038/s41578-021-00303-1
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