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Developing machine learning for heterogeneous catalysis with experimental and computational data
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DOI：10.1038/s41570-025-00740-4.png)
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Abstract
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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. Machine learning aids heterogeneous catalysis research by linking performance to physicochemical controllable properties. This Review discusses experimental and computational high-throughput and machine learning approaches, comparing them by modelling method, features, dataset size, accuracy and reaction type.
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