Accelerating Biomaterial Discovery via Deep Learning and Multiscale Modeling

Overview

We are developing a unified framework to accelerate biomaterial discovery, particularly in biomaterial design, by integrating deep learning with multiscale modeling. Graph neural networks (GNNs) are used to capture intricate spatial and relational features of material structures and molecular assemblies, while large language models (LLMs) trained on sequences provide contextual priors and generative capacity for novel designs. Diffusion models further enable the conditional and controllable generation of molecular details, aligning molecular configurations with desired properties. To ensure scalability and rapid design cycles, all components are optimized for high-performance and parallel computing environments, enabling fast exploration of material design spaces and integration with experimental feedback.