AI-Based Forecasting of Polymer Properties for High-Temperature Butyl Acrylate Polymerizations

High-temperature polymerizations involving self-initiation
of the
monomer are attractive because of high reaction rate, comparable lower
viscosities, and no need for an additional initiator. However, the
polymers obtained show a more complex microstructure, e.g., with specific
branching levels or significant amounts of macromonomer. Simulations
of the polymerization processes are powerful tools to gain a deeper
understanding of the processes and the elemental reactions at the
molecular level. However, simulations can be computationally demanding,
requiring significant time and memory resources. Therefore, this study
aims at applying AI-based forecasting of tailored polymer properties
and using a kinetic Monte Carlo simulator for the generation of training
and test data. The applied machine learning (ML) models (random forest
and kernel density (KD) regression) predict monomer concentration,
macromonomer content, and full molar mass distributions as a function
of time, as well as the average branching level with an excellent
performance (R2 (coefficient of determination)
> 0.99, MAE (mean absolute error) < 1% for kernel density regression).
This study explores the number of training data needed for reliable
and accurate predictions in ML models. Explainability methods reveal
that the importance of input variables in ML models aligns with expert
knowledge.