Transferring a molecular foundation model for polymer property
  predictions
Pei Zhang Logan Kearney Debsindhu Bhowmik Zachary Fox Amit K. Naskar John Gounley
Transformer-based large language models have remarkable potential to
accelerate design optimization for applications such as drug development and
materials discovery. Self-supervised pretraining of transformer models requires
large-scale datasets, which are often sparsely populated in topical areas such
as polymer science. State-of-the-art approaches for polymers conduct data
augmentation to generate additional samples but unavoidably incurs extra
computational costs. In contrast, large-scale open-source datasets are
available for small molecules and provide a potential solution to data scarcity
through transfer learning. In this work, we show that using transformers
pretrained on small molecules and fine-tuned on polymer properties achieve
comparable accuracy to those trained on augmented polymer datasets for a series
of benchmark prediction tasks.