Recent advances in machine learning (ML) have shown promise in accelerating the discovery of polymers with desired properties by aiding in tasks such as virtual screening via property prediction. However, progress in polymer ML is hampered by the scarcity of high-quality labeled datasets, which are necessary for training supervised ML models. In this work, we study the use of the very recent ‘Joint Embedding Predictive Architecture’ (JEPA), a type of architecture developed for self-supervised learning (SSL), on polymer molecular graphs to understand whether pretraining with the proposed SSL strategy improves downstream performance when labeled data is scarce. We first pretrain our polymer-JEPA model on a large dataset of conjugated copolymer photocatalysts. The pretrained model is then fine-tuned on two distinct downstream tasks: predicting electron affinity in the same chemical space and classifying phase behavior in diblock copolymers, a different chemical space. Our results indicate that JEPA-based self-supervised pretraining enhances downstream performance, particularly when labeled data is very scarce, achieving improvements across both tested datasets. The method provides performance gains in cross-domain fine-tuning, highlighting its potential to extract general knowledge across different classes of polymers. By leveraging large amounts of unlabeled polymer structures for pretraining, the proposed strategy can further reduce the dependence on extensive labeled datasets.

The demand for innovative synthetic polymers with improved properties is high, but their structural complexity and vast design space hinder rapid discovery. Machine learning-guided molecular design is a promising approach to accelerate polymer discovery. However, the scarcity of labeled polymer data and the complex hierarchical structure of synthetic polymers make generative design particularly challenging. We advance the current state-of-the-art approaches to generate not only repeating units, but monomer ensembles including their stoichiometry and chain architecture. We build upon a recent polymer representation that includes stoichiometries and chain architectures of monomer ensembles and develop a novel variational autoencoder (VAE) architecture encoding a graph and decoding a string. Using a semi-supervised setup, we enable the handling of partly labelled datasets which can be beneficial for domains with a small corpus of labelled data. Our model learns a continuous, well organized latent space (LS) that enables de novo generation of copolymer structures including different monomer stoichiometries and chain architectures. In an inverse design case study, we demonstrate our model for in silico discovery of novel conjugated copolymer photocatalysts for hydrogen production using optimization of the polymer's electron affinity and ionization potential in the latent space.

Large chemical reaction data sets often suffer from incompleteness, such as missing molecules or stoichiometric information. Incomplete chemical reaction equations currently hinder us to perform automated mass balances across large sets of chemical reactions. In this work, we integrate two approaches for computational completion of partial reaction equations. Specifically, we combine a rule-based method and a machine learning model, a tailored version of the pre-trained Molecular Transformer, to complete reactions. The rule-based method takes sets of helper species into a linear solver and therewith balances some incomplete reactions. The machine learning model is trained to take partial reactions as inputs and predicts missing molecules and stoichiometries. We apply our methodology to the USPTO STEREO chemical reaction data set. The rule-based method completes about 50 % of the reactions. The language model shows a top 1 accuracy of 88.3 % on our test set and high validity (> 99 % of outputs are valid SMILES).