PolyConf: Unlocking Polymer Conformation Generation
through Hierarchical Generative Models

Polymer conformation generation is a critical task that enables atomic-level studies of diverse polymer materials. While significant advances have been made in designing various conformation generation methods for small molecules and proteins, these methods typically struggle to generate polymer conformations due to their unique structural characteristics. Meanwhile, the scarcity of polymer conformation datasets further limits the progress, making this important area largely unexplored. In this work, we propose PolyConf, a pioneering tailored polymer conformation generation method that leverages hierarchical generative models to unlock new possibilities. Specifically, we decompose the polymer conformation into a series of local conformations (i.e., the conformations of its repeating units), generating these local conformations through an autoregressive model, and then generating their orientation transformations via a diffusion model to assemble them into the complete polymer conformation. Moreover, we develop the first benchmark with a high-quality polymer conformation dataset derived from molecular dynamics simulations to boost related research in this area. The comprehensive evaluation consistently demonstrates that our PolyConf significantly outperforms existing conformation generation methods, thus facilitating advancements in polymer modeling and simulation.

In this work, we have devoted considerable time and resources to developing PolyBench, the first benchmark for polymer conformation generation. Specifically, PolyBench provides a high-quality polymer conformation dataset of over 50,000 polymers with conformations (about 2,000 atoms per conformation) derived from molecular dynamics simulations. Here, the number of repeating units within the polymer conformation ranges from approximately 20 to 100 for most polymers, with a small portion extending beyond 100.