poly BART : A Chemical Linguist for Polymer Property Prediction and Generative Design (2025)

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.

## introduction polymers play a crucial role in our daily lives, serving as essential constituents of countless materials and products that we rely on. designing novel application-specific polymers, however, continues to pose a significant challenge owing to the vast polymer chemical space. the application of machine learning (ml) in this space @xcite @xcite has made significant strides in addressing the forward problem of predicting material properties from polymer structures @xcite . in contrast, progress on the inverse problem, namely, rapidly designing polymers that meet target property requirements, has been far more limited, largely due to the inherent difficulty of generating chemically valid polymer structures with traditional ml. language models, owing to their generative capabilities, have emerged as an effective strategy for addressing both forward and inverse problems in the molecular space. drawing inspiration from foundation models in natural language processing (nlp), molecular foundation models @xcite are pretrained on the 'language' of molecules, typically represented as smiles @xcite , smarts (daylight chemical information systems), or selfies @xcite strings. these models are subsequently fine-tuned on downstream tasks, such as property prediction, hence solving the forward problem. the problem of generative design is often achieved by navigating the latent space learned during pretraining. the achievements of language models for molecules highlight a key opportunity for extending similar methodologies to polymers. particularly, the structural and representational parallels between the molecular and polymer domains present a unique possibility of adapting existing molecular foundation models to the polymer space. recognizing this, we develop polybart by strategically leveraging the chemical priors learned by existing molecular foundation models. to accomplish this, we introduce a novel representation, pseudo-polymer selfies (pselfies), an extension of selfies @xcite adapted for polymers. selfies (self-referencing embedded strings) is a robust molecular string representation that guarantees 100% syntactic validity, mean-ing every selfies string corresponds to a valid molecule. from a chemical standpoint, pselfies allow us to represent polymers in a format that mirrors molecular syntax, enabling direct compatibility with existing molecular language models and facilitating seamless continued pretraining. further details on this representation is found in section 3.1. we develop polybart through the continued pretraining of selfies-ted (ibm research), an encoder-decoder model based on bart @xcite and designed for molecular representations. polybart is a unique unifying model capable of solving both forward and inverse problems in polymer informatics. our work makes the following key contributions: ## polybart: a polymer foundation model in this section we detail the pselfies representation and introduce polybart. the overall model pipeline is shown in figure 1 . ## computational experiments in this section, we present results demonstrating the effectiveness of polybart on: (i) property prediction, (ii) hypothetical polymer generation, and (iii) property-guided never-seen-before polymer generation. we also highlight the ability of pselfies in leveraging pretrained knowledge through a comparative study. ## validation of design via laboratory synthesis and testing given the real-world implications of our work, it is important to extend our evaluation of polybart beyond computational benchmarks. thus, we perform experimental synthesis and characterization of a polymer candidate predicted by polybart small . we select one polymer with a high predicted t g and a favorable sa score and successfully synthesize it in the lab. following synthesis, we perform t g measurements to evaluate its thermal properties. the experimental protocol is detailed in appendix j. figure 4 presents the chemical structure of the synthesized polymer, along with the predicted and experimentally measured t g values. we confirm that the experimentally observed t g aligns well with the prediction. this successful synthesis of a high-t g polymer firmly establishes polybart's ability to guide real-world polymer design. ## conclusion in this work, we propose pselfies, a novel representation of polymers that enables the transfer of learned knowledge from molecular language models to the polymer space. we develop polybart small and polybart large , two polymer foundation models created through the continued pretraining of selfies-ted small and selfies-ted large , respectively. our results show that poly-bart performs comparably to state-of-the-art approaches in property prediction. moreover, poly-bart uniquely combines property prediction with generative design in a single unified framework and consistently produces numerous novel polymer structures. generated candidates not only satisfy target property criteria but also exhibit synthetic accessibility. by successfully synthesizing one such predicted polymer and experimentally confirming our predictions, we illustrate that language models can reliably drive real-world advances in polymer design. our work enables true bidirectional translation between polymer structures and properties and highlights the potential of language models in advancing polymer informatics.