Quantitative Structural Description of Zeolites by Machine Learning Analysis of Infrared Spectra

Application of machine learning (ML) algorithms to spectroscopic data has a great potential for obtaining hidden correlations between structural information and spectral features. Here, we apply ML algorithms to theoretically simulated infrared (IR) spectra to establish the structure-spectrum correlations in zeolites. Two hundred thirty different types of zeolite frameworks were considered in the study whose theoretical IR spectra were used as the training ML set. A classification problem was solved to predict the presence or absence of possible tilings and secondary building units (SBUs). Several natural tilings and SBUs were also predicted with an accuracy above 89%. The set of continuous descriptors was also suggested, and the regression problem was also solved using the ExtraTrees algorithm. For the latter problem, additional IR spectra were computed for the structures with artificially modified cell parameters, expanding the database to 470 different spectra of zeolites. The resulting prediction quality above or close to 90% was obtained for the average Si-O distances, Si-O- Si angles, and volume of TO4 tetrahedra. The obtained results provide new possibilities for utilization of infrared spectra as a quantitative tool for characterization of zeolites.

Automated summary

The application of machine learning algorithms to spectroscopic data has great potential in revealing hidden correlations between structural information and spectral features. In this study, ML algorithms were used to establish the connection between structure and spectra in zeolites by analyzing theoretically simulated infrared spectra. The accuracy of predicting the presence or absence of potential tilings and secondary building units (SBUs) was higher than 89%, and several natural tilings and SBUs were successfully predicted. Furthermore, regression analysis using the ExtraTrees algorithm achieved a prediction accuracy above or close to 90% for Si-O distances, Si-O-Si angles, and volume of TO4 tetrahedra. These results demonstrate the potential of infrared spectra as a quantitative tool for characterizing zeolites.