  Non-degradable plastic waste stays for decades on land and in water,
jeopardizing our environment; yet our modern lifestyle and current technologies
are impossible to sustain without plastics. Bio-synthesized and biodegradable
alternatives such as the polymer family of polyhydroxyalkanoates (PHAs) have
the potential to replace large portions of the world's plastic supply with
cradle-to-cradle materials, but their chemical complexity and diversity limit
traditional resource-intensive experimentation. In this work, we develop
multitask deep neural network property predictors using available experimental
data for a diverse set of nearly 23000 homo- and copolymer chemistries. Using
the predictors, we identify 14 PHA-based bioplastics from a search space of
almost 1.4 million candidates which could serve as potential replacements for
seven petroleum-based commodity plastics that account for 75% of the world's
yearly plastic production. We discuss possible synthesis routes for these
identified promising materials. The developed multitask polymer property
predictors are made available as a part of the Polymer Genome project at
https://PolymerGenome.org.
