Data-Driven Prediction of Janus/Core-Shell Morphology in Polymer Particles: A Machine-Learning Approach.

Abstract

The majority of research on Janus particles prepared by solvent evaporation-induced phase separation technique uses models based on interfacial tension or free energy to predict Janus/core-shell morphology. Data-driven predictions, in contrast, utilize multiple samples to identify patterns and outliers. Using machine-learning algorithms and explainable artificial intelligence (XAI) analysis, we developed a model based on a 200-instance data set to predict particle morphology. As model features, simplified molecular input line entry system syntax identifies explanatory variables, including cohesive energy density, molar volume, the Flory-Huggins interaction parameter of polymers, and the solvent solubility parameter. Our most accurate ensemble classifiers predict morphology with an accuracy of 90%. In addition, we employ innovative XAI tools to interpret system behavior, suggesting phase-separated morphology to be most affected by solvent solubility, polymer cohesive energy difference, and blend composition. While polymers with cohesive energy densities above a certain threshold favor the core-shell structure, systems with weak intermolecular interactions favor the Janus structure. The correlation between molar volume and morphology suggests that increasing the size of polymer repeating units favors Janus particles. Additionally, the Janus structure is preferred when the Flory-Huggins interaction parameter exceeds 0.4. XAI analysis introduces feature values that generate the thermodynamically low driving force of phase separation, resulting in kinetically stable morphologies as opposed to thermodynamically stable ones. The Shapley plots of this study also reveal novel methods for creating Janus or core-shell particles based on solvent evaporation-induced phase separation by selecting feature values that strongly favor a given morphology.