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Bioplastic design using multitask deep neural networks
Summary
Researchers used deep neural networks trained on nearly 23,000 polymer chemistries to identify 14 biodegradable bioplastics — made from biological sources rather than petroleum — that could replace the seven most commonly used synthetic plastics, which account for 75% of global plastic production. This AI-driven approach accelerates the search for eco-friendly plastic alternatives that naturally break down rather than fragmenting into persistent microplastics.
Abstract Non-degradable plastic waste jeopardizes our environment, yet our modern lifestyle and current technologies are impossible to sustain without plastics. Bio-synthesized and biodegradable alternatives such as 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. Here, we develop multitask deep neural network property predictors using available experimental data for a diverse set of nearly 23,000 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 also discuss possible synthesis routes for the identified promising materials.
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