The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Toward a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning
Open accessAbstract
Developing active and stable oxygen evolution catalysts is a key to enabling various future energy technologies and the state-of-the-art catalyst is Ir-containing oxide materials. Understanding oxygen chemistry on oxide materials is significantly more complicated than studying transition metal catalysts for two reasons: the most stable surface coverage under reaction conditions is extremely important but difficult to understand without many detailed calculations, and there are many possible active sites and configurations on O* or OH* covered surfaces. We have developed an automated and high-throughput approach to solve this problem and predict OER overpotentials for arbitrary oxide surfaces. We demonstrate this for a number of previously-unstudied IrO2 and IrO3 polymorphs and their facets. We discovered that low index surfaces of IrO2 other than rutile (110) are more active than the most stable rutile (110), and we identified promising active sites of IrO2 and IrO3 that outperform rutile (110) by 0.2 V in theoretical overpotential. Based on findings from DFT calculations, we pro- vide catalyst design strategies to improve catalytic activity of Ir based catalysts and demonstrate a machine learning model capable of predicting surface coverages and site activity. This work highlights the importance of investigating unexplored chemical space to design promising catalysts.
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Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials · doi:10.1063/1.4812323 · Cited by 13,094
A fast and robust algorithm for Bader decomposition of charge density
Henkelman, G., Arnaldsson, A., & Jónsson, H. (2006) · Computational Materials Science · doi:10.1016/j.commatsci.2005.04.010 · Cited by 10,723
Benchmarking Heterogeneous Electrocatalysts for the Oxygen Evolution Reaction
McCrory, C. C. L., Jung, S., Peters, J. C., & Jaramillo, T. F. (2013) · Journal of the American Chemical Society · doi:10.1021/ja407115p · Cited by 7,026
Universality in Oxygen Evolution Electrocatalysis on Oxide Surfaces
Man, I. C., Su, H., Calle‐Vallejo, F., Hansen, H. A., Martínez, J. I., Inoglu, N. G., Kitchin, J., Jaramillo, T. F., Nørskov, J. K., & Rossmeisl, J. (2011) · ChemCatChem · doi:10.1002/cctc.201000397 · Cited by 4,769
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
Ong, S. P., Richards, W. D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., Persson, K. A., & Ceder, G. (2013) · Computational Materials Science · doi:10.1016/j.commatsci.2012.10.028 · Cited by 4,534
How copper catalyzes the electroreduction of carbon dioxide into hydrocarbon fuels
Peterson, A. A., Abild-Pedersen, F., Studt, F., Rossmeisl, J., & Nørskov, J. K. (2010) · Energy & Environmental Science · doi:10.1039/c0ee00071j · Cited by 3,784
Xie, T., & Grossman, J. C. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.145301 · Cited by 2,828
Homogeneously dispersed multimetal oxygen-evolving catalysts
Zhang, B., Zheng, X., Voznyy, O., Comin, R., Bajdich, M., García-Melchor, M., Han, L., Xu, J., Liu, M., Zheng, L., et al. (2016) · Science · doi:10.1126/science.aaf1525 · Cited by 2,447
Tran, K., & Ulissi, Z. W. (2018) · Nature Catalysis · doi:10.1038/s41929-018-0142-1 · Cited by 920
Self-citationHighly Active and Stable Iridium Pyrochlores for Oxygen Evolution Reaction
Lebedev, D., Povia, M., Waltar, K., Abdala, P. M., Castelli, I. E., Fabbri, E., Blanco, M. V., Fedorov, A., Copéret, C., Marzari, N., et al. (2017) · Chemistry of Materials · doi:10.1021/acs.chemmater.7b00766 · Cited by 228
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Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609
The Sabatier Principle in Electrocatalysis: Basics, Limitations, and Extensions
Ooka, H., Huang, J., & Exner, K. S. (2021) · Frontiers in Energy Research · doi:10.3389/fenrg.2021.654460 · Cited by 600
Electrochemical Synthesis of H2O2 by Two-Electron Water Oxidation Reaction
Shi, X., Back, S., Gill, T. M., Siahrostami, S., & Zheng, X. (2021) · Chem · doi:10.1016/j.chempr.2020.09.013 · Cited by 429
Self-citationLi, L., Cao, X., Huo, J., Qu, J., Chen, W., Liu, C., Zhao, Y., Liu, H., & Wang, G. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2022.09.022 · Cited by 404
Recent Advances in Non‐Noble Bifunctional Oxygen Electrocatalysts toward Large‐Scale Production
Zeng, K., Zheng, X., Li, C., Yan, J., Tian, J., Jin, C., Strasser, P., & Yang, R. (2020) · Advanced Functional Materials · doi:10.1002/adfm.202000503 · Cited by 365
Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex
Friederich, P., dos Passos Gomes, G., De Bin, R., Aspuru-Guzik, A., & Balcells, D. (2020) · Chemical Science · doi:10.1039/d0sc00445f · Cited by 179
tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes
Balcells, D., & Skjelstad, B. B. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01041 · Cited by 143
Maley, S. M., Kwon, D.-H., Rollins, N., Stanley, J. C., Sydora, O. L., Bischof, S. M., & Ess, D. H. (2020) · Chemical Science · doi:10.1039/d0sc03552a · Cited by 81
Bracconi, M., & Maestri, M. (2020) · Chemical Engineering Journal · doi:10.1016/j.cej.2020.125469 · Cited by 48
Metadata
- Authors
- Back, S., Tran, K., & Ulissi, Z. W.
- Venue
- ACS Catalysis
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-07-15
- Volume
- 9
- Issue
- 9
- Pages
- 7651-7659
- ISSN
- 2155-5435 (ISSN-L)
- OpenAlex
- W2959406148
- Cited by
- 178
- References
- 10
- FWCI
- 4.33
- Type
- journal article
- Language
- en
- Open access
- green
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