Bioplastic design using multitask deep neural networks

Journal Article · Sat Dec 03 00:00:00 UTC 2022 · Communications Materials

DOI: https://doi.org/10.1038/s43246-022-00319-2 · OSTI ID:1902538

Kuenneth, Christopher ; Lalonde, Jessica; Marrone, Babetta L.; Iverson, Carl N.; Ramprasad, Rampi; Pilania, Ghanshyam

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.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: Alexander von Humboldt Foundation; Office of Naval Research (ONR); USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1902538
• Report Number(s): LA-UR-22-21336; 96; PII: 319
• Journal Information: Communications Materials, Journal Name: Communications Materials Journal Issue: 1 Vol. 3; ISSN 2662-4443
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United Kingdom
• Language: English