polyBART: A Chemical Linguist for Polymer Property Prediction and Generative Design

TL;DR

polyBART is a pioneering language model for polymers that enables accurate property prediction and generative design, validated through computational and laboratory experiments, including the first synthesis of a model-designed polymer.

Contribution

It introduces PSELFIES for polymer representation and demonstrates bidirectional translation between polymer structures and properties, advancing generative polymer design.

Findings

Achieved state-of-the-art polymer property prediction.

First successful synthesis of a language model-designed polymer.

Validated predictions with laboratory experiments.

Abstract

Designing polymers for targeted applications and accurately predicting their properties is a key challenge in materials science owing to the vast and complex polymer chemical space. While molecular language models have proven effective in solving analogous problems for molecular discovery, similar advancements for polymers are limited. To address this gap, we propose polyBART, a language model-driven polymer discovery capability that enables rapid and accurate exploration of the polymer design space. Central to our approach is Pseudo-polymer SELFIES (PSELFIES), a novel representation that allows for the transfer of molecular language models to the polymer space. polyBART is, to the best of our knowledge, the first language model capable of bidirectional translation between polymer structures and properties, achieving state-of-the-art results in property prediction and design of novel polymers for electrostatic energy storage. Further, polyBART is validated through a combination of both computational and laboratory experiments. We report what we believe is the first successful synthesis and validation of a polymer designed by a language model, predicted to exhibit high thermal degradation temperature and confirmed by our laboratory measurements. Our work presents a generalizable strategy for adapting molecular language models to the polymer space and introduces a polymer foundation model, advancing generative polymer design that may be adapted for a variety of applications.