Predictive and Generative Artificial intelligence towards Polymers
Date:
Understanding the three-dimensional conformation of polymers is essential for connecting molecular structure to macroscopic material properties. However, generating reliable polymer conformations remains a major challenge due to their structural flexibility, diversity, and limited availability of high-quality reference data. Building on our initial work predicting polymer properties with its monomer structure, we present our following work, PolyConf, a generative modeling framework that predicts polymer conformations directly from molecular graphs.