A Comparison of Machine Learning Approaches in Predicting Viscosity for Partially Hydrolyzed Polyacrylamide Derivatives

Abstract

Partially hydrolyzed polyacrylamides (HPAM) are widely used to modulate the viscosity of formulations. The appropriate application of a viscosity model can facilitate the idealization of new macromolecules and contribute to a better understanding of the structure-property relationship. In the present study, machine learning approaches, Multiple Linear Regression (MLR) and Random Forest (RF), were compared to model the viscosifying effect of HPAM derivatives, based on their chemical composition and concentration in an aqueous solution. The evaluated data come from a previous experimental study, which explores a post-synthetic polymer modification methodology. The relative importance of the variables was investigated, determining the features with the greatest influence on viscosity, including variations in chemical composition, with emphasis on the more hydrophobic groups (C7 and C12). The accuracy of the models was evaluated using statistical criteria, the coefficient of determination (R2) and the Root Mean Square Error (RMSE). The Random Forest approach outperformed Multiple Linear Regression, with values of 0.97 and 0.30 for R2 and RMSE, respectively, compared to 0.83 and 0.67 for Multiple Linear Regression. Applying the Random Forest model, it was possible to generate a set of hypothetical macromolecules, with potential viscosifying effects. These macromolecules were idealized focusing on mixed compositions of C7 and C12 with a maximum structural variation of 10 mol%. Additionally, this structural mapping provided insights for designing promising polymers by inserting cyclic structures, such as CYCLOPROP, which could overcome the solubility limitation observed in the literature.

Perovskite Solar Cell: Chemical Composition and Bandgap Energy via Machine Learning

“…The Random Forest algorithm is particularly suited for handling high-dimensional data and can model complex non-linear relationships (Keller et al, 2019). Moreover, Random Forest is known for its robustness and flexibility, being less susceptible to overfitting compared to other algorithms (Shah et al, 2020;Da Silveira et al, 2023;Santos et al, 2023). Support Vector Machine, in turn, has been recognized as a robust tool in machine learning due to its ability to handle large dimensional spaces and its effectiveness in finding optimized separation margins.…”

“…Computational intelligence, with an emphasis on machine learning, has gained considerable attention in the discovery of new materials. Advanced algorithms can make fast and accurate predictions, allowing the identification of material compositions with desired properties more efficiently and economically (Schmidt et al, 2019;Butler et al, 2018;Shi et al, 2018;Da Silveira et al, 2023;Hui et al, 2023;Li et al, 2023). While the use of machine learning in predicting material properties is not novel, its application specifically in predicting the bandgap energy of perovskites remains emergent and holds great potential for optimizing the performance of photovoltaic devices.…”