# Copolymer Informatics with Multi-Task Deep Neural Networks

arXiv: https://arxiv.org/abs/2103.14174 · HTML: https://mlsanity.com/paper/2103.14174/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 25 Mar 2021.

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

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Polymer Informatics: Current Status and Critical Next Steps](https://mlsanity.com/paper/2011.00508/) (2020)
- [Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders](https://mlsanity.com/paper/2011.02551/) (2020)
- [Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation](https://mlsanity.com/paper/2002.01563/) (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)
- [A graph representation of molecular ensembles for polymer property prediction](https://mlsanity.com/paper/2205.08619/) (2022)
- [Transferring a molecular foundation model for polymer property predictions](https://mlsanity.com/paper/2310.16958/) (2023)
- [Multi-Task Multi-Fidelity Learning of Properties for Energetic Materials](https://mlsanity.com/paper/2408.14488/) (2024)
- [Data Fusion of Deep Learned Molecular Embeddings for Property Prediction](https://mlsanity.com/paper/2504.07297/) (2025)
- [Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processing](https://mlsanity.com/paper/2402.19462/) (2024)
- [AI-Driven Discovery of High Performance Polymer Electrodes for Next-Generation Batteries](https://mlsanity.com/paper/2502.13899/) (2025)

## Abstract (arXiv)

Polymer informatics tools have been recently gaining ground to efficiently and effectively develop, design, and discover new polymers that meet specific application needs. So far, however, these data-driven efforts have largely focused on homopolymers. Here, we address the property prediction challenge for copolymers, extending the polymer informatics framework beyond homopolymers. Advanced polymer fingerprinting and deep-learning schemes that incorporate multi-task learning and meta-learning are proposed. A large data set containing over 18,000 data points of glass transition, melting, and degradation temperature of homopolymers and copolymers of up to two monomers is used to demonstrate the copolymer prediction efficacy. The developed models are accurate, fast, flexible, and scalable to more copolymer properties when suitable data become available.