Leveraging Data-Driven strategy for Accelerating the Discovery of
Polyesters with Targeted Glass Transition Temperatures
Abstract
To overcome the limitations of empirical synthesis and expedite the
discovery of new polymers, this work aims to develop a data-driven
strategy for profoundly aiding in the design and screening of novel
polyester materials. Initially, we collected 695 polyesters with their
associated glass transition temperatures (Tgs) to develop a quantitative
structure-property relationship (QSPR) model. The model underwent
rigorous validation (external validation, internal validation, Y-random
and application domain analysis) to demonstrate its robust predictive
capabilities and high stability. Subsequently, by employing an in-silico
retrosynthesis strategy, over 95000 virtual polyesters were designed,
largely expanding the available space for polyester materials. External
assessments highlight the good extrapolation ability of the QSPR model.
Furthermore, we experimentally synthesized diverse virtual polyesters
with Tgs covering a sufficient large temperature range. It is believed
that this data-driven approach can drive future product development of
polymer industry.