Objective, Accurate, and Efficient Modeling of Macromolecular Assembly Behaviors via an AI-Powered Automated Dissipative Particle Dynamics Framework

The diverse phase behaviors of macromolecules fundamentally govern their mesoscopic morphologies and macroscopic functional properties, broadening their utility across diverse applications. While dissipative particle dynamics (DPD) is crucial for characterizing mesoscale phase behaviors and thermodynamic properties, its predictive power in complex systems is severely restricted. This limitation stems from the subjective nature of traditional coarse-grained (CG) mapping and the computational exigencies required for accurate parametrization. Herein, we introduce one-button DPD (OB-DPD), an automated framework that integrates graph-theoretic CG mapping with artificial intelligence-driven prediction to facilitate seamless, high-fidelity parametrization. It drastically compresses the traditional multiday preprocessing cycle into seconds. We demonstrate OB-DPD’s robustness across diverse systems, ranging from small molecules to complex polymers. Furthermore, we successfully predicted and experimentally validated the assembly behaviors of a novel triblock copolymer. By streamlining the workflow from molecular design to characterization, this data-driven framework accelerates the development of macromolecular materials.