Unlocking azobenzene isomerization mechanisms via an LLM agent-driven workflow integrating simulation, experiment, and machine learning

= 0.99 and MAE = 5°. Attention maps highlight mechanistically informative bands and support holistic (non-marker-dependent) interpretation; transfer learning extends performance across chemical environments and experimental datasets. LLM agents formulated the research plan and coordinated automated simulations and measurements, whereas neural-network architecture design, training, and comparative benchmarking were performed by human researchers to retain full flexibility for model exploration and ensure rigorous interpretation. To our knowledge, this is the first LLM-agent-planned and -orchestrated mechanistic study unifying literature synthesis, theory, experiment, and machine learning. The resulting strategy advances quantitative insight into azobenzene photoisomerization and provides a generalizable blueprint for AI-driven investigations of dynamic molecular systems.