Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO 2 Reduction
Open accessAbstract
Bimetallic catalysts are promising for the most difficult thermal and electrochemical reactions, but modeling the many diverse active sites on polycrystalline samples is an open challenge. We present a general framework for addressing this complexity in a systematic and predictive fashion. Active sites for every stable low-index facet of a bimetallic crystal are enumerated and cataloged, yielding hundreds of possible active sites. The activity of these sites is explored in parallel using a neural-network-based surrogate model to share information between the many density functional theory (DFT) relaxations, resulting in activity estimates with an order of magnitude fewer explicit DFT calculations. Sites with interesting activity were found and provide targets for follow-up calculations. This process was applied to the electrochemical reduction of CO 2 on nickel gallium bimetallics and indicated that most facets had similar activity to Ni surfaces, but a few exposed Ni sites with a very favorable on-top CO configuration. This motif emerged naturally from the predictive modeling and represents a class of intermetallic CO 2 reduction catalysts. These sites rationalize recent experimental reports of nickel gallium activity and why previous materials screens missed this exciting material. Most importantly these methods suggest that bimetallic catalysts will be discovered by studying facet reactivity and diversity of active sites more systematically.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Recent studies have used machine learning to explore the complex reaction network, find active bimetallic facets for CO2RR, or identify alloys for CO2RR and HER.
“There are a few recent studies using machine learning to explore the complex reaction network, [66] find active bimetallic facets for CO2RR, [67] or identify alloys for CO2RR and HER, [68]”
From Electrochemical CO <sub>2</sub> Reduction into Chemical Feedstocks: From Mechanistic Electrocatalysis Models to System Design (2019) · cites this paper as [67]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
(2014) · Cited by 33,419 · see citing works
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471 · see citing works
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Behler, J., & Parrinello, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.146401 · Cited by 5,010 · see citing works
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. (2012) · Computational Materials Science · doi:10.1016/j.commatsci.2012.10.028 · Cited by 4,339 · see citing works
Surface energies of elemental crystals
Tran, R., Xu, Z., Radhakrishnan, B., Winston, D., Sun, W. Q., Persson, K. A., & Ong, S. P. (2016) · Scientific Data · doi:10.1038/sdata.2016.80 · Cited by 1,052 · see citing works
International Conference on Machine Learning (ICML-99).
Scheffer, T. (1999) · Künstliche Intell. · Cited by 1,011 · see citing works
Understanding trends in electrochemical carbon dioxide reduction rates
Liu, X., Xiao, J., Peng, H., Hong, X., Chan, K., & Nørskov, J. K. (2017) · Nature Communications · doi:10.1038/ncomms15438 · Cited by 805 · see citing works
Self-citationFireWorks: a dynamic workflow system designed for high‐throughput applications
Jain, A., Ong, S. P., Chen, W., Medasani, B., Qu, X., Kocher, M., Brafman, M., Petretto, G., Rignanese, G., Hautier, G., et al. (2015) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.3505 · Cited by 548 · see citing works
Campbell, C. T. (1990) · Annual Review of Physical Chemistry · doi:10.1146/annurev.pc.41.100190.004015 · Cited by 513 · see citing works
Calle‐Vallejo, F., Martínez, J. I., García-Lastra, J. M., Sautet, P., & Loffreda, D. (2014) · Angewandte Chemie International Edition · doi:10.1002/anie.201402958 · Cited by 504 · see citing works
Artrith, N., & Urban, A. (2016) · Computational Materials Science · doi:10.1016/j.commatsci.2015.11.047 · Cited by 503 · see citing works
Chen, J. G., Menning, C. A., & Zellner, M. B. (2008) · Surface Science Reports · doi:10.1016/j.surfrep.2008.02.001 · Cited by 498 · see citing works
Representing high-dimensional potential-energy surfaces for reactions at surfaces by neural networks
Lorenz, S., Groß, A., & Scheffler, M. (2004) · Chemical Physics Letters · doi:10.1016/j.cplett.2004.07.076 · Cited by 400 · see citing works
Amp: A modular approach to machine learning in atomistic simulations
Khorshidi, A., & Peterson, A. A. (2016) · Computer Physics Communications · doi:10.1016/j.cpc.2016.05.010 · Cited by 357 · see citing works
Potential Dependence of Electrochemical Barriers from ab Initio Calculations
Chan, K., & Nørskov, J. K. (2016) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.6b00382 · Cited by 323 · see citing works
Self-citationTorelli, D. A., Francis, S. A., Crompton, J. C., Javier, A., Thompson, J. R., Brunschwig, B. S., Soriaga, M. P., & Lewis, N. S. (2016) · ACS Catalysis · doi:10.1021/acscatal.5b02888 · Cited by 306 · see citing works
Self-citationArtrith, N., & Kolpak, A. M. (2014) · Nano Letters · doi:10.1021/nl5005674 · Cited by 204 · see citing works
Orbitalwise Coordination Number for Predicting Adsorption Properties of Metal Nanocatalysts
Ma, X., & Xin, H. (2017) · Physical Review Letters · doi:10.1103/physrevlett.118.036101 · Cited by 172 · see citing works
Bifunctional alloys for the electroreduction of CO 2 and CO
Hansen, H. A., Shi, C., Lausche, A. C., Peterson, A. A., & Nørskov, J. K. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c5cp07717f · Cited by 150 · see citing works
Self-citationA high-throughput framework for determining adsorption energies on solid surfaces
Montoya, J. H., & Persson, K. A. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0017-z · Cited by 119 · see citing works
Artrith, N., & Kolpak, A. M. (2015) · Computational Materials Science · doi:10.1016/j.commatsci.2015.07.046 · Cited by 115 · see citing works
Kolb, B., Lentz, L. C., & Kolpak, A. M. (2017) · Scientific Reports · doi:10.1038/s41598-017-01251-z · Cited by 110 · see citing works
Equilibrium Crystal Shape of Ni from First Principles
Zhang, W., Chen, C., & Zhang, S.-Y. (2013) · The Journal of Physical Chemistry C · doi:10.1021/jp404569m · Cited by 84 · see citing works
The equilibrium crystal shape of nickel
Meltzman, H., Dominique Chatain, B., Avizemer, D., Besmann, T. M., & Kaplan, W. D. (2011) · Acta Materialia · doi:10.1016/j.actamat.2011.02.021 · Cited by 73 · see citing works
Flagged first, then by citations
Research works printed but not linked: 21
- Chen, J. G.; Menning, C. A.; Zellner, M. B. Surf. Sci. Rep. 2008, 63, 201 -254.
- Campbell, C. T. Annu. Rev. Phys. Chem. 1990, 41, 775-837.
- Hansen, H.; Shi, C.; Lausche, A.; Peterson, A.; Nørskov, J. Phys. Chem. Chem. Phys. 2016, 18, 9194-9201.
- Torelli, D. A.; Francis, S. A.; Crompton, J. C.; Javier, A.; Thompson, J. R.; Brun- schwig, B. S.; Soriaga, M. P.; Lewis, N. S. ACS Catal. 2016, 6, 2100-2104.
- Liu, X.; Xiao, J.; Peng, H.; Hong, X.; Chan, K.; Nørskov, J. K. Nat. Commun. 2017, 8, 15438.
- Ong, S. P.; Richards, W. D.; Jain, A.; Hautier, G.; Kocher, M.; Cholia, S.; Gunter, D.; Chevrier, V. L.; Persson, K. A.; Ceder, G. Comp. Mater. Sci. 2013, 68, 314 -319.
- Zhang, W.-B.; Chen, C.; Zhang, S.-Y. J. Phys. Chem. C 2013, 117, 21274-21280.
- Meltzman, H.; Chatain, D.; Avizemer, D.; Besmann, T. M.; Kaplan, W. D. Acta Mater. 2011, 59, 3473 -3483.
- Montoya, J. H.; Persson, K. A. npj Comput. Mater. 2017, 3, 14.
- Calle-Vallejo, F.; Martnez, J. I.; Garca-Lastra, J. M.; Sautet, P.; Loffreda, D. Angew. Chem., Int. Ed. 2014, 53, 8316-8319.
- Ma, X.; Xin, H. Phys. Rev. Lett. 2017, 118, 036101.
- Lorenz, S.; Gro, A.; Scheffler, M. Chemical Physics Letters 2004, 395, 210 -215.
- Artrith, N.; Kolpak, A. M. Computational Materials Science 2015, 110, 20 -28.
- Behler, J.; Parrinello, M. Phys. Rev. Lett. 2007, 98, 146401.
- Khorshidi, A.; Peterson, A. A. Comp. Phys. Comm. 2016, 207, 310-324.
- Kolb, B.; Lentz, L. C.; Kolpak, A. M. Sci. Rep. 2017, 7, 1192.
- Artrith, N.; Urban, A. Comp. Mater. Sci. 2016, 114, 135 -150.
- Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R. J. of Mach. Lear. Res. 2014, 15, 1929-1958.
- Abadi, M.; Agarwal, A.; Barham, P.; Brevdo, E.; Chen, Z.; Citro, C.; Corrado, G. S.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Goodfellow, I.; Harp, A.; Irving, G.; Isard, M.; Jia, Y.; Jozefowicz, R.; Kaiser, L.; Kudlur, M.; Levenberg, J.; Mané, D.; Monga, R.; Moore, S.; Murray, D.; Olah, C.; Schuster, M.; Shlens, J.; Steiner, B.; Sutskever, I.; Talwar, K.; Tucker, P.; Vanhoucke, V.; Vasudevan, V.; Viégas, F.; Vinyals, O.; Warden, P.; Wattenberg, M.; Wicke, M.; Yu, Y.; Zheng, X. Tensor- Flow: Large-Scale Machine Learning on Heterogeneous Systems. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI). 2016; pp 265-283.
- Jain, A.; Ong, S. P.; Chen, W.; Medasani, B.; Qu, X.; Kocher, M.; Brafman, M.; Petretto, G.; Rignanese, G.-M.; Hautier, G. Concurrency and Computation: Practice and Experience 2015, 27, 5037-5059.
- Chan, K.; Nørskov, J. K. J. Phys. Chem. Lett. 2016, 7, 1686.
Cited by
Sorted by citations
Progress and perspectives of electrochemical CO2 reduction on copper in aqueous electrolyte
Nitopi, S., Bertheussen, E., Scott, S. B., Liu, X., Engstfeld, A. K., Horch, S., Seger, B., Stephens, I. E. L., Chan, K., Hahn, C., et al. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00705 · Cited by 5,223 · see citing works
Self-citationData-driven prediction of battery cycle life before capacity degradation
Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., et al. (2019) · Nature Energy · doi:10.1038/s41560-019-0356-8 · Cited by 3,228 · see citing works
Kibria, M. G., Edwards, J. P., Gabardo, C. M., Dinh, C., Seifitokaldani, A., Sinton, D., & Sargent, E. H. (2019) · Advanced Materials · doi:10.1002/adma.201807166 · Cited by 1,331 · see citing works
Designing materials for electrochemical carbon dioxide recycling
Ross, M. B., De Luna, P., Li, Y., Dinh, C.-T., Kim, D., Yang, P., & Sargent, E. H. (2019) · Nature Catalysis · doi:10.1038/s41929-019-0306-7 · Cited by 1,302 · see citing works
Tran, K., & Ulissi, Z. W. (2018) · Nature Catalysis · doi:10.1038/s41929-018-0142-1 · Cited by 920 · see citing works
Self-citationVasileff, A., Xu, C., Jiao, Y., Zheng, Y., & Qiao, S. (2018) · Chem · doi:10.1016/j.chempr.2018.05.001 · Cited by 902 · see citing works
Four Generations of High-Dimensional Neural Network Potentials
Behler, J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00868 · Cited by 892 · see citing works
Fan, L., Xia, C., Yang, F., Wang, J., Wang, H., & Lü, Y. (2020) · Science Advances · doi:10.1126/sciadv.aay3111 · Cited by 768 · see citing works
Recent advances in carbon dioxide utilization
Zhang, Z., Pan, S.-Y., Li, H., Cai, J., Olabi, A. G., Anthony, E. J., & Manovic, V. M. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109799 · Cited by 739 · see citing works
Understanding heterogeneous electrocatalytic carbon dioxide reduction through operando techniques
Handoko, A. D., Wei, F., Jenndy, Yeo, B. S., & Seh, Z. W. (2018) · Nature Catalysis · doi:10.1038/s41929-018-0182-6 · Cited by 739 · see citing works
Theory-guided design of catalytic materials using scaling relationships and reactivity descriptors
Zhao, Z.-J., Liu, S., Zha, S., Cheng, D., Studt, F., Henkelman, G., & Gong, J. (2019) · Nature Reviews Materials · doi:10.1038/s41578-019-0152-x · Cited by 657 · see citing works
Machine Learning for Catalysis Informatics: Recent Applications and Prospects
Toyao, T., Maeno, Z., Takakusagi, S., Kamachi, T., Takigawa, I., & Shimizu, K.-I. (2020) · ACS Catalysis · doi:10.1021/acscatal.9b04186 · Cited by 617 · see citing works
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 613 · see citing works
A Critical Review of Machine Learning of Energy Materials
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 · see citing works
Gas-Diffusion Electrodes for Carbon Dioxide Reduction: A New Paradigm
Higgins, D. C., Hahn, C., Xiang, C., Jaramillo, T. F., & Weber, A. Z. (2018) · ACS Energy Letters · doi:10.1021/acsenergylett.8b02035 · Cited by 609 · see citing works
Self-citationUsing nature’s blueprint to expand catalysis with Earth-abundant metals
Bullock, R. M., Chen, J. G., Gagliardi, L., Chirik, P. J., Farha, O. K., Hendon, C. H., Jones, C. W., Keith, J. A., Klosin, J., Minteer, S. D., et al. (2020) · Science · doi:10.1126/science.abc3183 · Cited by 603 · see citing works
Electrocatalytic Nitrogen Reduction at Low Temperature
Deng, J., Iñiguez, J. A., & Liu, C. (2018) · Joule · doi:10.1016/j.joule.2018.04.014 · Cited by 590 · see citing works
Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery
Mai, H., Le, T. C., Chen, D., Winkler, D. A., & Caruso, R. A. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00061 · Cited by 574 · see citing works
High-entropy alloys in electrocatalysis: from fundamentals to applications
Ren, J.-T., Chen, L., Wang, H.-Y., & Yuan, Z.-Y. (2023) · Chemical Society Reviews · doi:10.1039/d3cs00557g · Cited by 551 · see citing works
Design concept for electrocatalysts
Wang, Y., Zheng, X., & Wang, D. S. (2021) · Nano Research · doi:10.1007/s12274-021-3794-0 · Cited by 549 · see citing works
Machine learning for a sustainable energy future
Yao, Z., Lum, Y., Johnston, A. K., Mejía-Mendoza, L. M., Zhou, X., Wen, Y., Aspuru‐Guzik, A., Sargent, E. H., & Seh, Z. W. (2022) · Nature Reviews Materials · doi:10.1038/s41578-022-00490-5 · Cited by 541 · see citing works
High-Entropy Alloys as Catalysts for the CO 2 and CO Reduction Reactions
Pedersen, J. K., Batchelor, T. A. A., Bagger, A., & Rossmeisl, J. (2020) · ACS Catalysis · doi:10.1021/acscatal.9b04343 · Cited by 497 · see citing works
Multi‐Sites Electrocatalysis in High‐Entropy Alloys
Li, H., Lai, J., Li, Z., & Wang, L. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202106715 · Cited by 494 · see citing works
Microkinetic Modeling: A Tool for Rational Catalyst Design
Motagamwala, A. H., & Dumesic, J. A. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00394 · Cited by 488 · see citing works
The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
Yao, K., Herr, J. E., Toth, D. W., Mckintyre, R., & Parkhill, J. (2018) · Chemical Science · doi:10.1039/c7sc04934j · Cited by 475 · see citing works
Stolarczyk, J. K., Bhattacharyya, S., Polavarapu, L., & Feldmann, J. (2018) · ACS Catalysis · doi:10.1021/acscatal.8b00791 · Cited by 467 · see citing works
Computational Methods in Heterogeneous Catalysis
Chen, B. W. J., Xu, L., & Mavrikakis, M. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01060 · Cited by 451 · see citing works
Machine learning for heterogeneous catalyst design and discovery
Goldsmith, B. R., Esterhuizen, J. A., Liu, J., Bartel, C. J., & Sutton, C. A. (2018) · AIChE Journal · doi:10.1002/aic.16198 · Cited by 446 · see citing works
Grosse, P., Gao, D., Scholten, F., Sinev, I. V., Mistry, H., & Cuenya, B. R. (2018) · Angewandte Chemie International Edition · doi:10.1002/anie.201802083 · Cited by 430 · see citing works
Zhou, T., Song, Z., & Sundmacher, K. (2019) · Engineering · doi:10.1016/j.eng.2019.02.011 · Cited by 390 · see citing works
Machine learning: Accelerating materials development for energy storage and conversion
Chen, A., Zhang, X., & Zhou, Z. P. (2020) · InfoMat · doi:10.1002/inf2.12094 · Cited by 373 · see citing works
Machine Learning for Computational Heterogeneous Catalysis
Schlexer, P., Winther, K. T., Torres, J. A. G., Streibel, V., Zhao, M., Bajdich, M., Abild‐Pedersen, F., & Bligaard, T. (2019) · ChemCatChem · doi:10.1002/cctc.201900595 · Cited by 358 · see citing works
Non-carbon-supported single-atom site catalysts for electrocatalysis
Zheng, X., Li, P., Dou, S. X., Sun, W., Pan, H., Wang, D. S., & Li, Y. (2021) · Energy & Environmental Science · doi:10.1039/d1ee00248a · Cited by 346 · see citing works
First-principles-based multiscale modelling of heterogeneous catalysis
Bruix, A., Margraf, J. T., Andersen, M., & Reuter, K. (2019) · Nature Catalysis · doi:10.1038/s41929-019-0298-3 · Cited by 338 · see citing works
Electrocatalytic carbon dioxide reduction: from fundamental principles to catalyst design
Zhang, X., Guo, S., Gandionco, K. A., Bond, A. M., & Zhang, J. (2020) · Materials Today Advances · doi:10.1016/j.mtadv.2020.100074 · Cited by 328 · see citing works
Yan, Y., Borhani, T. N. G., Subraveti, S. G., Pai, K. N., Prasad, V., Rajendran, A., Nkulikiyinka, P., Asibor, J. O., Zhang, Z., Shao, D., et al. (2021) · Energy & Environmental Science · doi:10.1039/d1ee02395k · Cited by 314 · see citing works
Machine learning for renewable energy materials
Gu, G. H., Noh, J., Kim, I., & Jung, Y. (2019) · Journal of Materials Chemistry A · doi:10.1039/c9ta02356a · Cited by 311 · see citing works
Yu, J., Wang, J., Ma, Y., Zhou, J., Wang, Y., Lu, P., Yin, J., Ye, R., Zhu, Z., & Fan, Z. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202102151 · Cited by 311 · see citing works
Zheng, X., Li, B., Wang, Q., Wang, D. S., & Li, Y. (2022) · Nano Research · doi:10.1007/s12274-022-4429-9 · Cited by 305 · see citing works
Toe, C. Y., Tsounis, C., Zhang, J., Masood, H., Gunawan, D., Scott, J. A., & Amal, R. (2021) · Energy & Environmental Science · doi:10.1039/d0ee03116j · Cited by 263 · see citing works
New aspects of operando Raman spectroscopy applied to electrochemical CO2 reduction on Cu foams
Jiang, S. H., Klingan, K., Pasquini, C., & Dau, H. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5054109 · Cited by 254 · see citing works
Towards operando computational modeling in heterogeneous catalysis
Grajciar, L., Heard, C. J., Bondarenko, A. A., Polynski, M. V., Meeprasert, J., Pidko, E. A., & Nachtigall, P. (2018) · Chemical Society Reviews · doi:10.1039/c8cs00398j · Cited by 251 · see citing works
Extracting Knowledge from Data through Catalysis Informatics
Medford, A. J., Kunz, M. R., Ewing, S. M., Borders, T. L., & Fushimi, R. (2018) · ACS Catalysis · doi:10.1021/acscatal.8b01708 · Cited by 244 · see citing works
Beyond Scaling Relations for the Description of Catalytic Materials
Andersen, M., Levchenko, S. V., Scheffler, M., & Reuter, K. (2019) · ACS Catalysis · doi:10.1021/acscatal.8b04478 · Cited by 242 · see citing works
Tuning Cu/Cu 2 O Interfaces for the Reduction of Carbon Dioxide to Methanol in Aqueous Solutions
Chang, X., Wang, T., Zhao, Z., Yang, P., Greeley, J., Mu, R., Zhang, G., Gong, Z., Luo, Z., Chen, J., et al. (2018) · Angewandte Chemie International Edition · doi:10.1002/anie.201805256 · Cited by 235 · see citing works
Application of Artificial Neural Networks for Catalysis: A Review
Li, H., Zhang, Z., & Liu, Z. (2017) · Catalysts · doi:10.3390/catal7100306 · Cited by 231 · see citing works
Machine Learning in Catalysis, From Proposal to Practicing
Yang, W., Fidelis, T. T., & Sun, W. (2019) · ACS Omega · doi:10.1021/acsomega.9b03673 · Cited by 226 · see citing works
Surface restructuring and predictive design of heterogeneous catalysts
Tao, F. F., & Salmerón, M. (2024) · Science · doi:10.1126/science.adq0102 · Cited by 222 · see citing works
Cu-Zn-based alloy/oxide interfaces for enhanced electroreduction of CO2 to C2+ products
Zhang, Z.-Y., Tian, H., Bian, L., Liu, S.-Z., Liu, Y., & Wang, Z. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2023.04.034 · Cited by 220 · see citing works
Surface organometallic chemistry in heterogeneous catalysis
Samantaray, M. K., Pump, E., Bendjériou, A., D’Elia, V., Pelletier, J. D. A., Guidotti, M., Psaro, R., & Basset, J. (2018) · Chemical Society Reviews · doi:10.1039/c8cs00356d · Cited by 215 · see citing works
Understanding carbon dioxide activation and carbon–carbon coupling over nickel
Vogt, C., Monai, M., Sterk, E. B., Palle, J., Melcherts, A. E. M., Zijlstra, B., Groeneveld, E., Berben, P. H., Boereboom, J. M., Hensen, E. J. M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12858-3 · Cited by 215 · see citing works
Machine learning meets volcano plots: computational discovery of cross-coupling catalysts
Meyer, B., Sawatlon, B., Heinen, S., von Lilienfeld, O. A., & Corminboeuf, C. (2018) · Chemical Science · doi:10.1039/c8sc01949e · Cited by 209 · see citing works
Cao, Y., Zhang, H., Ji, S., Sui, Z., Jiang, Z., Wang, D. S., Zaera, F., Zhou, X., Duan, X., & Li, Y. (2020) · Angewandte Chemie International Edition · doi:10.1002/anie.202004966 · Cited by 204 · see citing works
Ab Initio Machine Learning in Chemical Compound Space
Huang, B., & von Lilienfeld, O. A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01303 · Cited by 201 · see citing works
Data‐Driven Materials Innovation and Applications
Wang, Z., Sun, Z., Yin, H., Liu, X., Wang, J., Zhao, H., Pang, C. H., Wu, T., Li, S., Yin, Z., et al. (2022) · Advanced Materials · doi:10.1002/adma.202104113 · Cited by 193 · see citing works
Self-citationTheory-guided electrocatalyst engineering: From mechanism analysis to structural design
Zhang, M., Zhang, K., Ai, X., Liang, X., Zhang, Q., Chen, H., & Zou, X. (2022) · CHINESE JOURNAL OF CATALYSIS (CHINESE VERSION) · doi:10.1016/s1872-2067(22)64103-2 · Cited by 188 · see citing works
Ma, S., & Liu, Z. (2020) · ACS Catalysis · doi:10.1021/acscatal.0c03472 · Cited by 182 · see citing works
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 · see citing works
High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery
McCullough, K., Williams, T., Mingle, K., Jamshidi, P., & Lauterbach, J. A. (2020) · Physical Chemistry Chemical Physics · doi:10.1039/d0cp00972e · Cited by 180 · see citing works
Lu, Z., Chen, Z., & Singh, C. V. (2020) · Matter · doi:10.1016/j.matt.2020.07.029 · Cited by 173 · see citing works
Daiyan, R., Saputera, W. H., Masood, H., Leverett, J., Lu, X., & Amal, R. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201902106 · Cited by 172 · see citing works
Zhu, S., Delmo, E. P., Li, T., Qin, X., Tian, J., Zhang, L., & Shao, M. (2021) · Advanced Materials · doi:10.1002/adma.202005484 · Cited by 162 · see citing works
Shi, Z., Yang, W., Deng, X., Cai, C., Yan, Y., Liang, H., Liu, Z., & Qiao, Z. (2020) · Molecular Systems Design & Engineering · doi:10.1039/d0me00005a · Cited by 157 · see citing works
Modeling Operando Electrochemical CO 2 Reduction
Dattila, F., Seemakurthi, R. R., Zhou, Y., & López, N. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00690 · Cited by 157 · see citing works
Banerjee, S., Zhang, Z.-Q., Hall, A. S., & Thoi, V. S. (2020) · ACS Catalysis · doi:10.1021/acscatal.0c02387 · Cited by 155 · see citing works
Machine learning in solid heterogeneous catalysis: Recent developments, challenges and perspectives
Guan, Y., Chaffart, D., Liu, G., Tan, Z., Zhang, D., Wang, Y., Li, J., & Ricardez‐Sandoval, L. (2021) · Chemical Engineering Science · doi:10.1016/j.ces.2021.117224 · Cited by 155 · see citing works
Emerging Strategies for CO 2 Photoreduction to CH 4 : From Experimental to Data‐Driven Design
Cheng, S., Sun, Z., Lim, K. H., Gani, T. Z. H., Zhang, T., Wang, Y., Yin, H., Liu, K., Guo, H., Du, T., et al. (2022) · Advanced Energy Materials · doi:10.1002/aenm.202200389 · Cited by 153 · see citing works
Zaza, L., Rossi, K., & Buonsanti, R. (2022) · ACS Energy Letters · doi:10.1021/acsenergylett.2c00035 · Cited by 150 · see citing works
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 · see citing works
Machine Learning Boosts the Design and Discovery of Nanomaterials
Jia, Y., Hou, X., Wang, Z., & Hu, X. (2021) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.1c00483 · Cited by 143 · see citing works
Improving the Accuracy of Atomistic Simulations of the Electrochemical Interface
Sundararaman, R., Vigil‐Fowler, D., & Schwarz, K. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00800 · Cited by 138 · see citing works
Singh, S., Pareek, M., Changotra, A., Banerjee, S., Bhaskararao, B., Balamurugan, P., & Sunoj, R. B. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1916392117 · Cited by 132 · see citing works
Recent advances in knowledge discovery for heterogeneous catalysis using machine learning
Günay, M. E., & Yıldırım, R. (2020) · Catalysis Reviews · doi:10.1080/01614940.2020.1770402 · Cited by 136 · see citing works
Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning
Liu, J., Luo, W., Wang, L., Zhang, J., Fu, X., & Luo, J. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202110748 · Cited by 129 · see citing works
Liu, X., Zhang, X., Chen, X., Zhu, G., Yan, C., Huang, J., & Peng, H. (2021) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2021.12.004 · Cited by 127 · see citing works
Self-citationBeyond Density Functional Theory: The Multiconfigurational Approach To Model Heterogeneous Catalysis
Gaggioli, C. A., Stoneburner, S. J., Cramer, C. J., & Gagliardi, L. (2019) · ACS Catalysis · doi:10.1021/acscatal.9b01775 · Cited by 122 · see citing works
Lan, J., Palizhati, A., Shuaibi, M., Wood, B. M., Wander, B., Das, A., Uyttendaele, M., Zitnick, C. L., & Ulissi, Z. W. (2023) · npj Computational Materials · doi:10.1038/s41524-023-01121-5 · Cited by 125 · see citing works
Self-citationEnhancing the connection between computation and experiments in electrocatalysis
Resasco, J., Abild‐Pedersen, F., Hahn, C., Bao, Z., Koper, M. T. M., & Jaramillo, T. F. (2022) · Nature Catalysis · doi:10.1038/s41929-022-00789-0 · Cited by 124 · see citing works
Self-citationYang, Z. K., & Gao, W. (2022) · Advanced Science · doi:10.1002/advs.202106043 · Cited by 122 · see citing works
Sawant, V. A., Deshmukh, R., & Awati, C. J. (2022) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2022.11.012 · Cited by 124 · see citing works
Probing Atomic Distributions in Mono- and Bimetallic Nanoparticles by Supervised Machine Learning
Timoshenko, J., Wrasman, C. J., Luneau, M., Shirman, T., Cargnello, M., Bare, S. R., Aizenberg, J., Friend, C. M., & Frenkel, A. I. (2018) · Nano Letters · doi:10.1021/acs.nanolett.8b04461 · Cited by 121 · see citing works
Machine learning for design principles for single atom catalysts towards electrochemical reactions
Tamtaji, M., Gao, H., Hossain, M. D., Galligan, P. R., Wong, H., Liu, Z., Liu, H., Cai, Y., Goddard, W. A., & Luo, Z. (2022) · Journal of Materials Chemistry A · doi:10.1039/d2ta02039d · Cited by 119 · see citing works
Grosse, P., Gao, D., Scholten, F., Sinev, I. V., Mistry, H., & Cuenya, B. R. (2018) · Angewandte Chemie · doi:10.1002/ange.201802083 · Cited by 116 · see citing works
Roy, D., Mandal, S. C., & Pathak, B. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c16696 · Cited by 117 · see citing works
Active Sites in Heterogeneous Catalytic Reaction on Metal and Metal Oxide: Theory and Practice
Pan, Y., Shen, X., Yao, L., Bentalib, A., & Peng, Z. (2018) · Catalysts · doi:10.3390/catal8100478 · Cited by 116 · see citing works
Kim, K., Ward, L. T., He, J., Krishna, A., Agrawal, A., & Wolverton, C. M. (2018) · Physical Review Materials · doi:10.1103/physrevmaterials.2.123801 · Cited by 114 · see citing works
Zhang, Y., Guo, S., Zhang, X., Bond, A. M., & Zhang, J. (2020) · Nano Today · doi:10.1016/j.nantod.2019.100835 · Cited by 115 · see citing works
Artrith, N., Lin, Z., & Chen, J. G. (2020) · ACS Catalysis · doi:10.1021/acscatal.0c02089 · Cited by 114 · see citing works
Machine Learning in Screening High Performance Electrocatalysts for CO2 Reduction
Zhang, N., Yang, B., Liu, K., Li, H., Chen, G., Qiu, X., Li, W., Hu, J., Fu, J., Jiang, Y., et al. (2021) · Small Methods · doi:10.1002/smtd.202100987 · Cited by 114 · see citing works
High-Throughput Screening of Hydrogen Evolution Reaction Catalysts in MXene Materials
Zheng, J., Sun, X., Qiu, C., Yan, Y., Yao, Z., Deng, S., Zhong, X., Zhuang, G., Wei, Z., & Wang, J. (2020) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.0c02265 · Cited by 113 · see citing works
Chen, L., Tian, Y., Hu, X., Yao, S., Lu, Z., Chen, S., Zhang, X., & Zhou, Z. P. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202208418 · Cited by 113 · see citing works
Deep dive into machine learning density functional theory for materials science and chemistry
Fiedler, L., Shah, K., Bußmann, M., & Cangi, A. (2022) · Physical Review Materials · doi:10.1103/physrevmaterials.6.040301 · Cited by 117 · see citing works
Roy, D., Mandal, S. C., & Pathak, B. (2022) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.2c00929 · Cited by 112 · see citing works
Neural-network-enhanced evolutionary algorithm applied to supported metal nanoparticles
Kolsbjerg, E. L., Peterson, A. A., & Hammer, B. (2018) · Physical review. B./Physical review. B · doi:10.1103/physrevb.97.195424 · Cited by 109 · see citing works
Putri, L. K., Ng, B., Ong, W., Chai, S., & Mohamed, A. R. (2022) · Advanced Energy Materials · doi:10.1002/aenm.202201093 · Cited by 105 · see citing works
Huang, Y., Chen, Y., Cheng, T., Wang, L., & Goddard, W. A. (2018) · ACS Energy Letters · doi:10.1021/acsenergylett.8b01933 · Cited by 104 · see citing works
Yuan, H., Li, Z., Zeng, X. C., & Yang, J. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c00676 · Cited by 105 · see citing works
Predicting Chemical Reaction Barriers with a Machine Learning Model
Singh, A. R., Rohr, B. A., Gauthier, J. A., & Nørskov, J. K. (2019) · Catalysis Letters · doi:10.1007/s10562-019-02705-x · Cited by 103 · see citing works
Self-citationMachine Learning for Chemistry: Basics and Applications
Shi, Y., Yang, Z., Ma, S., Kang, P., Shang, C., Hu, P., & Liu, Z. (2023) · Engineering · doi:10.1016/j.eng.2023.04.013 · Cited by 106 · see citing works
Developing machine learning for heterogeneous catalysis with experimental and computational data
Bozal‐Ginesta, C., Pablo‐García, S., Choi, C., Tarancón, A., & Aspuru‐Guzik, A. (2025) · Nature Reviews Chemistry · doi:10.1038/s41570-025-00740-4 · Cited by 115 · see citing works
- Authors
- Ulissi, Z. W., Tang, M. T., Xiao, J., Liu, X., Torelli, D. A., Karamad, M., Cummins, K., Hahn, C., Lewis, N. S., Jaramillo, T. F., Chan, K., & Nørskov, J. K.
- Affiliations
- SLAC National Accelerator Laboratory (US)
- California Institute of Technology (US)
- Stanford University (US)
- Joint Center for Artificial Photosynthesis (US)
- Corresponding author
- Chan, Karen
- Nørskov, Jens K.
- ORCID
- Venue
- ACS Catalysis
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-08-31
- Volume
- 7
- Issue
- 10
- Pages
- 6600-6608
- ISSN
- 2155-5435 (ISSN-L)
- OpenAlex
- W2741906484
- Cited by
- 387
- References
- 24
- Citations by year
- 2026: 30 · 2025: 23 · 2024: 42 · 2023: 31 · 2022: 55 · 2021: 55 · 2020: 68 · 2019: 37 · 2018: 44 · 2017: 2
- FWCI
- 11.04
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- Locations
- ACS Catalysis (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- National Science Foundation (DGE-114747)
- U.S. Department of Energy (DE-AC02-05CH11231, DE-SC0004993)
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › CO2 Reduction Techniques and Catalysts
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Electrocatalysts for Energy Conversion
- Keywords
- bimetallic catalysts, CO2 reduction, active site identification, low-index facets, neural network surrogate model, intermetallic catalysts, electrochemical CO2 reduction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
