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

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.