# PolySet: Restoring the Statistical Ensemble Nature of Polymers for Machine Learning

arXiv: https://arxiv.org/abs/2512.13186 · HTML: https://mlsanity.com/paper/2512.13186/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [SELFIES and the future of molecular string representations](https://mlsanity.com/paper/2204.00056/) (2022)
- [polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics](https://mlsanity.com/paper/2209.14803/) (2022)
- [Polymer Informatics: Current Status and Critical Next Steps](https://mlsanity.com/paper/2011.00508/) (2020)
- [Graph Convolutional Neural Networks for Polymers Property Prediction](https://mlsanity.com/paper/1811.06231/) (2018)
- [PolyCL: Contrastive Learning for Polymer Representation Learning via Explicit and Implicit Augmentations](https://mlsanity.com/paper/2408.07556/) (2024)
- [A Physics-Enforced Neural Network to Predict Polymer Melt Viscosity](https://mlsanity.com/paper/2409.05240/) (2024)

## Abstract (arXiv)

Machine-learning (ML) models in polymer science typically treat a polymer as a single, perfectly defined molecular graph, even though real materials consist of stochastic ensembles of chains with distributed lengths. This mismatch between physical reality and digital representation limits the ability of current models to capture polymer behaviour. Here we introduce PolySet, a framework that represents a polymer as a finite, weighted ensemble of chains sampled from an assumed molar-mass distribution. This ensemble-based encoding is independent of chemical detail, compatible with any molecular representation and illustrated here in the homopolymer case using a minimal language model. We show that PolySet retains higher-order distributional moments (such as Mz, Mz+1), enabling ML models to learn tail-sensitive properties with greatly improved stability and accuracy. By explicitly acknowledging the statistical nature of polymer matter, PolySet establishes a physically grounded foundation for future polymer machine learning, naturally extensible to copolymers, block architectures, and other complex topologies.