Machine learning of organic solvents reveals an extraordinary axis in Hansen space as indicator of spherical precipitation of polymers

Abstract

AbstractMachine learning is an emerging tool in the field of materials chemistry for uncovering a principle from large datasets. Here, we focus on the spherical precipitation behavior of polymers and computationally extract a hidden trend that is orthogonal to the availability bias in the chemical space. For constructing a dataset, four polymers were precipitated from 416 solvent/nonsolvent combinations, and the morphology of the resulting precipitates were collected. The dataset was subjected to computational investigations consisting of principal component analysis and machine learning based on random forest model and support vector machine. Thereby, we eliminated the effect of the availability bias and found a linear combination of Hansen parameters to be the most suitable variable for predicting precipitation behavior. The predicted appropriate solvents are those with low hydrogen bonding capability, low polarity, and small molecular volume. Furthermore, we found that the capability for spherical precipitation is orthogonal to the availability bias and forms an extraordinary axis in Hansen space, which is the origin of the conventional difficulty in identifying the trend. The extraordinary axis points toward a void region, indicating the potential value of synthesizing novel solvents located therein.