Machine Learning and Big Data Analysis in the Catalysis Field

V. G. Filippov; Филиппов В. Г.; Y. A. Mikhailov; Михайлов Я. А.; A. V. Elyshev; Елышев А. В.

doi:10.31857/S0453881123020028

Machine Learning and Big Data Analysis in the Catalysis Field

Authors: Filippov V.G.¹, Mikhailov Y.A.¹, Elyshev A.V.¹
Affiliations:
1. University of Tyumen, TsyfroCatLab group
Issue: Vol 64, No 2 (2023)
Pages: 139-152
Section: ОБЗОРЫ
URL: https://ruspoj.com/0453-8811/article/view/660264
DOI: https://doi.org/10.31857/S0453881123020028
EDN: https://elibrary.ru/GMSQSK
ID: 660264

Cite item

Full Text

Open Access
Restricted Access

Access granted
Restricted Access

Subscription or Fee Access

Abstract

Recently, there has been a rapid development of experimental methods in the field of catalytic research, an increase in the amount of data that is difficult to process and objectively interpret. These methods will allow you to obtain the necessary information from experimental data using statistical approaches such as PCA, MCR, ALS. The use of new statistical and computational data processing methods will accelerate the development and implementation of catalytic technologies. At the same time, machine learning algorithms are beginning to be actively used to interpret and build descriptive models. This article will discuss the main methods of machine learning and their successful application for the analysis of infrared and X-ray absorption spectroscopy data.

Keywords

catalysis, IR spectroscopy, X-ray absorption spectroscopy, machine learning, computer simulation

References

Крупнов А.А., Погосбекян М.Ю. // Кинетика и катализ. 2019. Т. 60. № 2. С. 181.
Дюсембаева А.А., Вершинин В.И. // Кинетика и катализ. 2019. Т. 60. № 1. С. 129.
Landrum G.A., Penzotti J.E., Putta S. // Meas. Sci. Technol. 2005. V. 16. P. 270.
Erdem Günay M., Yıldırım R. // Catal. Rev. Sci. Eng. 2021. V. 63. P. 120.
Jollife I.T., Cadima J. // Philosophical Transactions of the Royal Society. A: Mathematical, Physical and Engineering Sciences. 2016. V. 374. P. 1.
Wei J., Cao S. // International Conference on Intelligent Computing and Control Systems (ICCS), IEEE, Secunderabad, India, 27–28 June 2019. P. 85.
Landrum G. A., Penzotti J. E., Putta S. // Meas. Sci. Technol. 2004. V. 16. № 1. P. 270.
Dai Q., Zhang C., Wu H. // Int. J. Database Theory and Application. 2016. V. 9. P. 1.
Somvanshi M., Chavan P. // International conference on computing communication control and automation (ICCUBEA), IEEE, Pune, Maharashtra, India, 12–13 August 2016. P. 1.
Jinnouchi R., Asahi R. // J. Phys. Chem. Lett. 2017. V. 8. P. 4279.
Saikia P., Baruah R.D., Singh S.K., Chaudhuri P.K. // Comput. Geosci. 2020. V. 135. P. 1.
Li H., Zhang Z., Liu Z. // Catalysts. 2017. V. 7. P. 1.
Yano J., Yachandra V.K. // Photosynth. Res. 2009. V. 102. P. 241.
Cibin G., Gianolio D., Parry S.A., Schoonjans T., Moore O., Draper R., Miller L.A., Thoma A., Doswell C.L., Graham A. // Radiat. Phys. Chem. 2020. V. 175. P. 1.
Mathew K., Zheng C., Winston D., Chen C., Dozier A., Rehr J.J., Ong S.P., Persson K.A. // Scientific Data. 2018. V. 5. P. 1.
Ravel B., Newville M. // J. Synchrotron Radiat. 2005. V. 12. P. 537.
Zheng C., Mathew K., Chen C., Chen Y., Tang H., Dozier A., Kas J.J., Vila F.D., Rehr J.J., Piper L.F.J., Persson K.A., Ong, S. P. // Comput. Mater. 2018. V. 4. P. 1.
Timoshenko J., Jeon H.S., Sinev I., Haase F.T., Herzog A., Cuenya B.R. // Chem. Sci. 2020. V. 11. P. 3727.
Timoshenko J. Lu D., Lin Y., Frenkel A.I. // J. Phys. Chem. Lett. 2017. V. 8. P. 5091.
Miyazato I., Takahashi L., Takahashi K. // Mol. Syst. Des. Eng. 2019. V. 4. P. 1014.
Weckhuysen B.M. // Phys. Chem. Chem. Phys. 2003. V. 5. P. 4351.
Serhan M., Sprowls M., Jackemeyer D., Long M., Perez I.D., Maret W., Forzani, E. // AIChE Annual Meeting, Conference Proceedings, 2019.
Penner-Hahn J.E. / In: Comprehensive Coordination Chemistry. II. Eds. Mccleverty J.A., Meyer T.J. Amsterdam–Oxford–New York–San Diego: Elsevier–Pergamon Press, 2003. V. 2. P. 159.
Wu L.B., Wu L.H., Yang W.M., Frenkel A.I. // Catal. Sci. Technol. 2014. V. 4. P. 2512.
Huth F., Schnell M., Wittborn J., Ocelic N., Hillenbrand R. // Nature Mater. 2011. V. 10. P. 352.
Ellis D.I., Broadhurst D., Clarke S.J., Goodacre R. // Analyst. 2005. V. 130. P. 1648.
Howley T., Madden M.G., O’Connell M.-L., Ryder A.G. // International Conference on Innovative Techniques and Applications of Artificial Intelligence. Cambridge, United Kingdom. 12–14 December 2005. P. 209.
Zou T., Dou Y., Mi H., Zou J., Ren Y. // Analyt. Biochem. 2006. V. 355. P. 1.
Kale M.J., Christopher P. // ACS Catal. 2016. V. 6. P. 5599.
Lansford J.L., Vlachos D.G. // Nature Commun. 2020. V. 11. P. 1.
Davies J.C., Nielsen R.M., Thomsen L.B., Chorkendorff I., Logadottir A., Lodziana Z., Besenbacher F. // Fuel Cells. 2004. V. 4. P. 309.
Steininger H., Lehwald S., Ibach H. // Surf. Sci. 1982. V. 123. P. 264.
Lansford J.L., Vlachos D.G. // ACS Nano. 2020. V. 14. P. 17295.
Agarwal N., Sondhi A., Chopra K., Singh G. / In: Tiwari S., Trivedi M., Mishra K., Misra A., Kumar K., Suryani, E. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing. Springer, Singapore, 2021. V. 1168. P. 145.
Chanussot L., Das A., Heras-Domingo J., Goyal S., Ho C., Lavril T., Palizhati A., Parikh D., Riviere M., Shuaibi M., Tran K., Ulissi Z., Yoon J., Zitnick C.L. // 2020 Virtual AIChE Annual Meeting.2020.