Machine Learning Models for Predicting Polymer Solubility in Solvents across Concentrations and Temperatures.

Abstract

Artificial intelligence and machine learning have become essential tools in predicting material properties to aid in the accelerated design of new materials. Polymer solubility, critical for new formulations and solution processing, is one such property. However, current models are limited by inadequate experimental data sets that cannot capture the complexity and detail for many features contributing to polymer solubility. Here, we provide a data set for polymer solution behavior based on Crystal16 turbidity measurements that includes high quality percent transmission data for polymer solutions for a variety of polymers, solvents, concentrations and temperatures. We use this data set to train a model that predicts the experimental transmission data at many temperatures and multiple concentrations. From this, we are able to classify the polymer/solvent pairs into three solubility categories providing a level of granularity to predictions beyond prior binary classification models considering only solvent/nonsolvent classes. The inclusion of multiple concentrations, temperatures and partially soluble data expands solubility prediction capability beyond prior work into predictions more attractive for use by formulators and process designers working with industrial polymer solutions.