Bioplastic Design using Multitask Deep Neural Networks
This work addresses the environmental issue of non-degradable plastic waste by enabling efficient discovery of bioplastic alternatives, though it is incremental as it applies existing deep learning methods to a new domain.
The researchers tackled the problem of finding biodegradable bioplastics to replace petroleum-based plastics by developing multitask deep neural network predictors using data from nearly 23,000 polymer chemistries, identifying 14 promising candidates from 1.4 million options that could replace 75% of global plastic production.
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.