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Machine Learning and Big Data Analysis in the Field of Catalysis (A Review)

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Abstract

Recently, the rapid development of experimental methods in the field of catalytic research allows for large amounts of data to be obtained. The use of new statistical and computational processing methods, including the extraction of information from experimental data and their unbiased interpretation, is important for accelerating the development and implementation of catalytic technologies. Necessary information can be extracted using statistical approaches such as PCA, MCR, and ALS. At the same time, machine learning algorithms are beginning to be actively used to interpret and build descriptive models. This paper discusses the main methods of machine learning and examples of their successful application to the analysis of infrared and X-ray absorption spectroscopic data.

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ACKNOWLEDGMENTS

The authors are grateful to E.A. Uslamin (Postdoc, Delft University of Technology) for his invaluable help in planning and preparing this article.

Funding

This study was supported by the Tyumen oblast as part of the implementation of a grant agreement in the form of a subsidy to nonprofit organizations no. 89-DON dated December 7, 2020.

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Authors and Affiliations

  1. University of Tyumen, TsyfroCatLab Group, 625003, Tyumen, Russia

    V. G. Filippov, Ya. A. Mikhailov & A. V. Elyshev

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  1. V. G. Filippov
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  2. Ya. A. Mikhailov
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  3. A. V. Elyshev
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Corresponding author

Correspondence to Ya. A. Mikhailov.

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Translated by V. Makhlyarchuk

Abbreviations and notation: PCA, principal component analysis; MCR, multivariate curve resolution; ALS, alternating least squares; XAS, X-ray adsorption spectroscopy; XANES, X-ray absorption near edge structure; EXAFS, extended X-ray absorption fine structure; AI, artificial intelligence; ML, machine learning; UML, unsupervised machine learning; SML, supervised machine learning; SVM, support vector machine; NIR, near infrared (range); GCN, generalized coordination number; MLR, multivariate linear regression; LR, logistic regression; DFT, density functional theory; PDF, probability distribution function; HPSTM, high-pressure scanning tunneling microscopy; DRIFT, diffuse reflectance infrared Fourier transform spectroscopy; ATR, attenuated total reflection IR spectroscopy; SVM, support vector machine; LEED, low energy electron diffraction; MS, mass spectrometry; TDS, thermal desorption spectroscopy; HREELS, high resolution electron energy loss spectroscopy; SERS, surface enhanced Raman spectroscopy; COOP, crystal orbital overlap population; FCC, face-centered cubic lattice; HCP, hexagonal close-packed lattice.

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Filippov, V.G., Mikhailov, Y.A. & Elyshev, A.V. Machine Learning and Big Data Analysis in the Field of Catalysis (A Review). Kinet Catal 64, 122–134 (2023). https://doi.org/10.1134/S0023158423020027

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  • DOI: https://doi.org/10.1134/S0023158423020027

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