posted on 2023-06-26, 23:35authored byYuta Aoki, Stephen Wu, Teruki Tsurimoto, Yoshihiro Hayashi, Shunya Minami, Okubo Tadamichi, Kazuya Shiratori, Ryo Yoshida
Predicting and understanding
the phase equilibria or phase separation
in polymer–solvent solutions represent unresolved fundamental
problems in polymer science. The phase behavior and thermodynamics
of polymer miscibility depend on the inter- and intramolecular interactions
of a polymer with a certain molecular weight distribution mixed with
a solvent. Here, we develop a machine-learning framework to achieve
highly generalized and robust prediction of Flory–Huggins χ
parameters for polymer–solvent solutions. The model was trained
using experimentally observed temperature-dependent χ parameters
for 1190 samples, comprising 46 unique polymers and 140 solvent species.
However, the difficulty was that the data set was quantitatively limited
and qualitatively biased owing to technical issues in determining
the Flory–Huggins χ parameters. To overcome these limitations,
we produced an in-house data set of χ parameters obtained from
quantum chemical calculations for thousands of polymer–solvent
pairs and a large list of soluble and insoluble polymer–solvent
pairs. Using these three data sets, we conducted multitask machine
learning that simultaneously performed the “soluble/insoluble”
classification and quantitative evaluation of both experimental and
calculated χ parameters. Consequently, we obtained a highly
generalized model applicable to a wide range of polymer solution spaces.
In this paper, the predictive power and physicochemical implications
of the model are demonstrated, along with quantitative comparisons
with existing methods.