Application of Machine Learning Methods to Predicting the Degree of Crystallinity of MFI Type Zeolites

Predicting the degree of crystallinity of zeolites from the initial synthesis parameters is an extremely difficult-to-solve problem. One of the ways to find the corresponding relationships is processing of data on the zeolite synthesis by machine learning algorithms. In this study, we analyzed 650 research papers and created a database including the parameters of the synthesis of MFI type zeolites and data on the degree of crystallinity of the material obtained. Finding relationships between the initial synthesis parameters and degree of crystallinity of the zeolite formed is a regression problem. In this study, it was solved by three machine learning algorithms: decision tree, random forest, and gradient boosting. To enhance the algorithm operation accuracy, we added to the initial dataset polynomial features of degrees 2-5. The gradient boosting algorithm based on data with third-degree polynomial features showed the highest accuracy in combination with the database processing rate. The mean absolute error (MAE) of the values given by the model relative to the real degrees of crystallinity was 10.3%.

Automated summary

In this study, we used machine learning algorithms to predict the degree of crystallinity of zeolites based on the initial synthesis parameters. By analyzing a large number of research papers and creating a database, the gradient boosting algorithm with polynomial features achieved the highest accuracy in combination with the database processing rate.