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Identifying a highly efficient molecular photocatalytic CO2 reduction system via descriptor-based high-throughput screening

Abstract

Molecular metal complexes offer opportunities for developing artificial photocatalytic systems. The search for efficient molecular photocatalytic systems, which involves a vast number of photosensitizer–catalyst combinations, is extremely time consuming via a conventional trial and error approach, while high-throughput virtual screening has not been feasible owing to a lack of reliable descriptors. Here we present a machine learning-accelerated high-throughput screening protocol for molecular photocatalytic CO2 reduction systems using multiple descriptors incorporating the photosensitization, electron transfer and catalysis steps. The protocol rapidly screened 3,444 molecular photocatalytic systems including 180,000 conformations of photosensitizers and catalysts during their interaction, enabling the prediction of six promising candidates. Then, we experimentally validated the screened photocatalytic systems, and the optimal one achieved a turnover number of 4,390. Time-resolved spectroscopy and first-principles calculation further validated not only the relevance of the descriptors within certain screening scopes but also the role of dipole coupling in triggering dynamic catalytic reaction processes.

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Fig. 1: ML-guided prediction of molecular photocatalytic system.
Fig. 2: Photocatalytic performance of the screened optimal system in CO2RR.
Fig. 3: Relevance of dipole coupling as descriptor and its role in triggering CO2RR.
Fig. 4: Schematic mechanistic pathway for CO2RR.

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Data availability

The crystallographic data for [CoIIL(H2O)2]ClClO4 (CCDC no. 2116350) can be obtained free of charge from the CCDC via www.ccdc.cam.ac.uk/structures. The atomic coordinates of the optimized computational models, the initial and final configurations in molecular dynamics simulations are provided as separate Supplementary Data files, respectively. Source data are provided with this paper.

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Acknowledgements

This work was supported by National Key R&D Programme of China (2020YFA0406103, Y.X.), NSFC (21725102, Y.X.; 91961106, Y.X.; 22422905, C.G.; 22175165, C.G.; 22025304, J.J.; 22033007, J.J.; and 22273093, G.Z.), Strategic Priority Research Programme of the CAS (XDPB14, Y.X.), Beijing Municipal Science and Technology Commission (grant no. Z191100001619005, Y.T.), CAS Project for Young Scientists in Basic Research (YSBR-005, J.J.), Open Funding Project of National Key Laboratory of Human Factors Engineering (no. SYFD062010K, Y.X.), Youth Innovation Promotion Association CAS (2021451, C.G.), USTC Research Funds of the Double First-Class Initiative (YD2060002020, C.G.) and Fundamental Research Funds for the Central Universities (20720220007, Y.X.; WK2340000104, C.G.; WK2400000004, C.G.). TR-XAS characterization was performed at the beamline 1W2B in the BSRF, China. We thank Q. Wan at the Technical Institute of Physics and Chemistry, Chinese Academy of Sciences for his help with the nanosecond transient absorption experimental tests. We thank S. Zhou at the University of Science and Technology of China for single-crystal X-ray diffraction measurements and crystal structure determination.

Author information

Authors and Affiliations

Authors

Contributions

Y.H. designed and performed the catalyst synthesis and photocatalytic reaction experiments. C.Y. designed and performed the TR-XAS experiments at BSRF and OTA characterization. S.W. performed the ML study. Q.W. performed the DFT calculations of free energy profiles. G.Z. supervised the theoretical calculations. M.R. performed the TR-XAS characterization experiments at SLAC and analysed the results. F.Z., H.W., D. Skoien and T.K. joined the discussion of TR-XAS experiment results. P.S. joined the OTA analysis. L.L. performed the PL characterization experiments. A.C. and G.L. helped to synthesize the catalysts. H.L. joined the molecule orbital analysis. Y.H., C.Y., S.W. and Q.W. analysed, discussed the experimental results and drafted the manuscript. C.G. and Y.X. proposed the research direction. D. Sokaras, C.G., J.J., Y.T. and Y.X. supervised the project, analysed and discussed the experimental results and revised the manuscript.

Corresponding authors

Correspondence to Dimosthenis Sokaras, Chao Gao, Jun Jiang, Ye Tao or Yujie Xiong.

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Nature Catalysis thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Summary of [CuI(P^P)(N^N)]+ photosensitizers in dataset.

The dataset contains 41 heteroleptic CuI photosensitizers (denoted as PS 1−41) by tuning the electronic and steric properties through ligand modification.

Extended Data Fig. 2 Summary of metal-complex catalysts in dataset.

The dataset contains 84 N-donor pentadentate macrocyclic metal-complex (MII, M = Co, Fe, Ni, Zn) catalysts (denoted as CAT 1−84) by altering the metal centre, axial ligand and modifying the second coordination sphere.

Extended Data Fig. 3 Machine learning-accelerated screening protocol.

A step-by-step screening strategy was used to screen the optimal photocatalytic system within the dataset containing 3,444 candidate combinations from an orthogonality of the 41 photosensitizers and the 84 catalysts.

Extended Data Fig. 4 Kinetic analysis of the signal decay in OTA spectra.

Conditions: 0.25 mM PS-1 photosensitizer and 10 mM BIH in the presence of 0.5 mM different metal-complex (MII, M = Co, Fe, Ni, Zn) catalysts. The signal decay was probed at 540 nm following 370 nm laser excitation (1 Hz, 11.6 mJ/pulse) in mixed MeCN/H2O (4:1, v/v) solution.

Source data

Supplementary information

Supplementary Information

Supplementary methods, note, discussion, Figs. 1–61, Tables 1–19 and references.

Supplementary Data 1

Source Data for Supplementary Figs.

Supplementary Data 2

DFT optimized coordinates of GS.

Supplementary Data 3.

DFT optimized coordinates of TI1.

Supplementary Data 4.

DFT optimized coordinates of MS.

Supplementary Data 5.

DFT optimized coordinates of TI2.

Supplementary Data 6.

DFT optimized coordinates of TI3.

Supplementary Data 7.

DFT optimized coordinates of TI4.

Supplementary Data 8.

Coordinate files of the initial and final configurations for all combinations in molecular dynamics simulations.

Supplementary Data 9.

cif file of CAT1.

Source data

Source Data Fig. 1.

Statistical source data.

Source Data Fig. 2.

Statistical source data.

Source Data Fig. 3.

Statistical source data.

Source Data Extended Data Fig. 4.

Statistical source data.

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Hu, Y., Yu, C., Wang, S. et al. Identifying a highly efficient molecular photocatalytic CO2 reduction system via descriptor-based high-throughput screening. Nat Catal 8, 126–136 (2025). https://doi.org/10.1038/s41929-025-01291-z

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