  Artificial intelligence-based methods are becoming increasingly effective at
screening libraries of polymers down to a selection that is manageable for
experimental inquiry. The vast majority of presently adopted approaches for
polymer screening rely on handcrafted chemostructural features extracted from
polymer repeat units -- a burdensome task as polymer libraries, which
approximate the polymer chemical search space, progressively grow over time.
Here, we demonstrate that directly "machine-learning" important features from a
polymer repeat unit is a cheap and viable alternative to extracting expensive
features by hand. Our approach -- based on graph neural networks, multitask
learning, and other advanced deep learning techniques -- speeds up feature
extraction by one to two orders of magnitude relative to presently adopted
handcrafted methods without compromising model accuracy for a variety of
polymer property prediction tasks. We anticipate that our approach, which
unlocks the screening of truly massive polymer libraries at scale, will enable
more sophisticated and large scale screening technologies in the field of
polymer informatics.
