Abstract
Machine learning techniques have emerged as a useful tool for identifying complex patterns and correlations in large datasets, such as associating catalyst performance to its physicochemical properties. In the heterogeneous catalysis communities, machine learning models have mostly been developed using high-throughput quantum chemistry calculations, with only a few case studies resulting in experimentally validated catalyst improvements. This limited success may be due to the use of simplified catalyst structures in computational studies and the lack of comprehensive experimental datasets. In this Review, we bring together studies integrating high-throughput approaches and machine learning for the advancement of solid heterogeneous catalysis, leveraging both experimental and computational data. We systematically analyse trends in the field, based on the descriptors used as model input and output; the materials, devices, or reactions investigated; the dataset size; and the overall achievements. Furthermore, for models reporting unitless R2 values, we compare the performances based on these mentioned trends.
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Acknowledgements
C.B.-G. acknowledges funding from a Marie Skłodowska Curie Actions Postdoctoral Fellowship grant (101064374). C.C. and S.P.-G. acknowledge that this material is based upon work supported by the U.S. Department of Energy, Office of Science, Subaward by “University of Minnesota, Project title: Development of Machine Learning and Molecular Simulation Approaches to Accelerate the Discovery of Porous Materials for Energy-Relevant Applications” under Award Number DE-SC0023454. A.T. acknowledges support from the Generalitat de Catalunya (2021-SGR-00750, NANOEN). A.A.-G. thanks A. G. Frøseth for his generous support. A.A.-G. also acknowledges the generous support from the Acceleration Consortium, the Natural Resources Canada and the Canada 150 Research Chairs programme.
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Bozal-Ginesta, C., Pablo-García, S., Choi, C. et al. Developing machine learning for heterogeneous catalysis with experimental and computational data. Nat Rev Chem (2025). https://doi.org/10.1038/s41570-025-00740-4
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DOI: https://doi.org/10.1038/s41570-025-00740-4
