The DOI matches its Crossref record; 379 of the 379 references it cites were found in a registry or an index.
Computational Methods in Heterogeneous Catalysis
Abstract
The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. We finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.
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1 retracted
RETRACTED ARTICLE: A universal principle for a rational design of single-atom electrocatalysts
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Cited by
Electrocatalysis in Alkaline Media and Alkaline Membrane-Based Energy Technologies
Yang, Y., Peltier, C. R., Zeng, R., Schimmenti, R., Li, Q., Huang, X., Yan, Z., Potsi, G., Selhorst, R., Lu, X., et al. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00331 · Cited by 544
Self-citationThe Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
Tran, R., Lan, J., Shuaibi, M., Wood, B. M., Goyal, S., Das, A., Heras-Domingo, J., Kolluru, A., Rizvi, A., Shoghi, N., et al. (2023) · ACS Catalysis · doi:10.1021/acscatal.2c05426 · Cited by 329
Bridging the complexity gap in computational heterogeneous catalysis with machine learning
Mou, T., Pillai, H. S., Wang, S., Wan, M., Han, X., Schweitzer, N. M., Che, F., & Xin, H. (2023) · Nature Catalysis · doi:10.1038/s41929-023-00911-w · Cited by 280
Computational chemistry for water-splitting electrocatalysis
Miao, L., Jia, W., Cao, X., & Jiao, L. (2024) · Chemical Society Reviews · doi:10.1039/d2cs01068b · Cited by 241
Quo vadis multiscale modeling in reaction engineering? – A perspective
Wehinger, G. D., Ambrosetti, M., Cheula, R., Ding, Z.-B., Isoz, M., Kreitz, B., Kuhlmann, K., Kutscherauer, M., Niyogi, K., Poissonnier, J., et al. (2022) · Chemical Engineering Research and Design · doi:10.1016/j.cherd.2022.05.030 · Cited by 64
Metadata
- Authors
- Chen, B. W. J., Xu, L., & Mavrikakis, M.
- Venue
- Chemical Reviews
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-12-22
- Volume
- 121
- Issue
- 2
- Pages
- 1007-1048
- ISSN
- 0009-2665 (ISSN-L)
- 1520-6890
- OpenAlex
- W3113496643
- Cited by
- 447
- References
- 379
- FWCI
- 1.53
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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