# Bioplastic Design using Multitask Deep Neural Networks

arXiv: https://arxiv.org/abs/2203.12033 · HTML: https://mlsanity.com/paper/2203.12033/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 70 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Polymer Informatics with Multi-Task Learning](https://mlsanity.com/paper/2010.15166/) (2020)
- [Polymer Informatics: Current Status and Critical Next Steps](https://mlsanity.com/paper/2011.00508/) (2020)

## Built on by (most cited first, 7 of 7)

- [polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics](https://mlsanity.com/paper/2209.14803/) (2022)
- [Inverse Design of Copolymers Including Stoichiometry and Chain Architecture](https://mlsanity.com/paper/2410.02824/) (2024)
- [Transferring a molecular foundation model for polymer property predictions](https://mlsanity.com/paper/2310.16958/) (2023)
- [AI-Driven Discovery of High Performance Polymer Electrodes for Next-Generation Batteries](https://mlsanity.com/paper/2502.13899/) (2025)
- [Multi-modal cascade feature transfer for polymer property prediction](https://mlsanity.com/paper/2505.03704/) (2025)
- [Sustainable Materials Discovery in the Era of Artificial Intelligence](https://mlsanity.com/paper/2601.21527/) (2026)
- [Periodic Topological Deep Learning for Polymer Design and Discovery](https://mlsanity.com/paper/2605.26833/) (2026)

## Abstract (arXiv)

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.