Bridging Visual Intuition and Chemical Expertise: A Virtual Collaborative Framework for Autonomous Nonadiabatic Dynamics Analysis

Abstract Analyzing nonadiabatic molecular dynamics trajectories traditionally heavily relies on expert intuition and visual pattern recognition, a process that is difficult to formalize. We present VisU, a vision-driven framework that leverages the complementary strengths of two state-of-the-art large language models (Doubao-Seed-1.6-Vision and DeepSeek-V3.2) to establish a “virtual research cooperation”. This operates through a “Mentor–Engineer–Student” paradigm that mimics the collaborative intelligence of a professional chemistry laboratory. Within this ecosystem, the Mentor provides physical intuition through visual reasoning, while the Engineer adaptively constructs analysis scripts, and the Student executes the pipeline and manages the data and results. VisU autonomously orchestrates a four-stage workflow comprising Preprocessing, Recursive Channel Discovery, Important-Motion Identification, and Validation/Summary. This systematic approach identifies reaction channels and key nuclear motions while generating a professional academic report at the end. By bridging visual insight with chemical expertise, VisU establishes a new framework for human–AI collaboration in the analysis of excited-state dynamics simulation results, significantly reducing dependence on manual interpretation and enabling more intuitive, scalable mechanistic discovery in nonadiabatic dynamics simulations.