# Polymer Informatics with Multi-Task Learning

arXiv: https://arxiv.org/abs/2010.15166 · HTML: https://mlsanity.com/paper/2010.15166/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 29 of 29)

- [polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics](https://mlsanity.com/paper/2209.14803/) (2022)
- [TransPolymer: a Transformer-based language model for polymer property predictions](https://mlsanity.com/paper/2209.01307/) (2022)
- [A graph representation of molecular ensembles for polymer property prediction](https://mlsanity.com/paper/2205.08619/) (2022)
- [MMPolymer: A Multimodal Multitask Pretraining Framework for Polymer Property Prediction](https://mlsanity.com/paper/2406.04727/) (2024)
- [Bioplastic Design using Multitask Deep Neural Networks](https://mlsanity.com/paper/2203.12033/) (2022)
- [PolyCL: Contrastive Learning for Polymer Representation Learning via Explicit and Implicit Augmentations](https://mlsanity.com/paper/2408.07556/) (2024)
- [Inverse Design of Copolymers Including Stoichiometry and Chain Architecture](https://mlsanity.com/paper/2410.02824/) (2024)
- [Graph Diffusion Transformers are In-Context Molecular Designers](https://mlsanity.com/paper/2510.08744/) (2025)
- [Transferring a molecular foundation model for polymer property predictions](https://mlsanity.com/paper/2310.16958/) (2023)
- [Representing Polymers as Periodic Graphs with Learned Descriptors for Accurate Polymer Property Predictions](https://mlsanity.com/paper/2205.13757/) (2022)
- [Multimodal machine learning with large language embedding model for polymer property prediction](https://mlsanity.com/paper/2503.22962/) (2025)
- [High-entropy Advantage in Neural Networks' Generalizability](https://mlsanity.com/paper/2503.13145/) (2025)
- [Polymer-Agent: Large Language Model Agent for Polymer Design](https://mlsanity.com/paper/2601.16376/) (2026)
- [Multi-Task Multi-Fidelity Learning of Properties for Energetic Materials](https://mlsanity.com/paper/2408.14488/) (2024)
- [Molecular topological deep learning for polymer property prediction](https://mlsanity.com/paper/2410.04765/) (2024)
- [Data Fusion of Deep Learned Molecular Embeddings for Property Prediction](https://mlsanity.com/paper/2504.07297/) (2025)
- [MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction](https://mlsanity.com/paper/2408.01426/) (2024)
- [Joint Embedding Predictive Architecture for self-supervised pretraining on polymer molecular graphs](https://mlsanity.com/paper/2506.18194/) (2025)
- [Topology-Aware Multiscale Mixture of Experts for Efficient Molecular Property Prediction](https://mlsanity.com/paper/2601.12637/) (2026)
- [Materials Informatics: An Algorithmic Design Rule](https://mlsanity.com/paper/2305.03797/) (2023)
- [From Abstract to Actionable: Pairwise Shapley Values for Explainable AI](https://mlsanity.com/paper/2502.12525/) (2025)
- [AI-Driven Discovery of High Performance Polymer Electrodes for Next-Generation Batteries](https://mlsanity.com/paper/2502.13899/) (2025)
- [What do Large Language Models know about materials?](https://mlsanity.com/paper/2507.14586/) (2025)
- [A Conformation-Centric Generative Foundation Model for Linear Polymer Modeling and Design](https://mlsanity.com/paper/2510.16023/) (2025)
- [Sustainable Materials Discovery in the Era of Artificial Intelligence](https://mlsanity.com/paper/2601.21527/) (2026)
- [Gaussian Process Regression-based Knowledge Distillation Framework for Simultaneous Prediction of Physical and Mechanical Properties of Epoxy Polymers](https://mlsanity.com/paper/2603.16925/) (2026)
- [Periodic Topological Deep Learning for Polymer Design and Discovery](https://mlsanity.com/paper/2605.26833/) (2026)
- [Molecular representation shapes the balance between target fidelity and exploration in flow based polymer generation](https://mlsanity.com/paper/2609.16028/) (2026)
- [An open benchmark for machine learning-based polymer property prediction](https://mlsanity.com/paper/2609.27036/) (2026)

## Abstract (arXiv)

Modern data-driven tools are transforming application-specific polymer development cycles. Surrogate models that can be trained to predict the properties of new polymers are becoming commonplace. Nevertheless, these models do not utilize the full breadth of the knowledge available in datasets, which are oftentimes sparse; inherent correlations between different property datasets are disregarded. Here, we demonstrate the potency of multi-task learning approaches that exploit such inherent correlations effectively, particularly when some property dataset sizes are small. Data pertaining to 36 different properties of over 13, 000 polymers (corresponding to over 23,000 data points) are coalesced and supplied to deep-learning multi-task architectures. Compared to conventional single-task learning models (that are trained on individual property datasets independently), the multi-task approach is accurate, efficient, scalable, and amenable to transfer learning as more data on the same or different properties become available. Moreover, these models are interpretable. Chemical rules, that explain how certain features control trends in specific property values, emerge from the present work, paving the way for the rational design of application specific polymers meeting desired property or performance objectives.