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

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.