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

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.