Strategy of Coupling Artificial Intelligence with Thermodynamic Mechanism for Predicting Complex Polymer Viscosities

Abstract

With the environmental protection requirements brought about by the large-scale application of polymers in industrial fields, understanding the viscosities of polymers is becoming increasingly important. The different arrangements and crystallinity of the polymers make their viscosities difficult to calculate. To address this challenge, new strategies based on artificial intelligence algorithms are proposed. First, the strategy trains three artificial intelligence algorithms [extreme gradient boosting (XGBoost), convolutional neural network (CNN), and multilayer perceptron (MLP)] based on molecular descriptors of the polymer molecular properties. Next, the PC-SAFT parameters are input into the XGBoost and CNN algorithms as molecular descriptors representing the thermodynamic properties of the polymer to improve the accuracy of the algorithm prediction results. Subsequently, the Molecular ACCess Systems chemical fingerprinting was combined with the XGboost algorithm and CNN algorithm to further improve the accuracy of predicting viscosities. The XGboost algorithm was identified as the best predictive algorithm for predicting the viscosities of the polymer in different states. This discovery is expected to provide effective information for screening polymers for applications in medicine and the chemical industry.