Machine Learning Algorithms for Predicting Complexation Properties of Weak Polyelectrolytes

Abstract

Predicting the complexation properties of weak polyelectrolytes presents substantial challenges due to their partial ionization in solution and sensitivity to environmental conditions. The intricacies of these systems extend to the influence of molecular structure, size, and various other factors. To address this issue, we propose a comprehensive framework that leverages various machine learning algorithms, including regression models, decision trees, random forests, neural networks, support vector machines, Gaussian processes, k-nearest neighbors, and ensemble methods. The methodology involves several stages: data collection, feature engineering, model training, validation, testing, and interpretation. Data collected either from experiments or simulations are used to train the models, where features such as molecular weight, degree of ionization, and crosslink density are engineered to capture the essence of complexation behavior. The selected machine learning algorithms then facilitate the understanding and prediction of complexation properties under diverse conditions, including varying pH levels and ionic strengths. Importantly, we emphasize the critical role of domain-specific knowledge to interpret machine learning predictions effectively, ensuring they are aligned with physical and chemical principles. This multi-algorithmic approach offers an advanced toolset for the complexation study of weak polyelectrolytes, promising better predictive performance and interpretability than traditional methods.