Control Flow Operators in PyTorch
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
Representation systems for polymers are a constant issue in deep-learning models for polymer property prediction, necessitating a balance between structural accuracy with interoperability to achieve utility in property prediction tasks. To facilitate this, we introduce a serialized polymer graph (SPG) notation and SPG-TED289M, a SPG-based foundation model for polymers, which has been pre-trained on a carefully curated dataset of 1 million SPG samples. To better handle the unique characteristics of SPG, we extended the tokenization process, resulting in a vocabulary of 2,407 distinct tokens. We evaluated the SPG-TED289M model's performance across a range of tasks including copolymer phase behavior, polymer membrane properties, multi-task learning, refractive index prediction, ionic conductivity, gas permeability, and glass transition temperature. The model demonstrated state-of-the-art performance in most of these areas, achieving results on par with specialized models designed for specific tasks. This indicates that SPG-TED289M, with minimal fine-tuning, can adapt effectively to complex polymer-related tasks, showcasing its robustness and versatility as a foundation model. The SPG-TED289M model provides significant flexibility and scalability, making it a valuable tool for various applications in polymer science.
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Kristjan Greenewald, Yuancheng Yu, et al.
NeurIPS 2024
Ben Fei, Jinbai Liu
IEEE Transactions on Neural Networks