Recently, Lan et al. have developed a hierarchical protocol using unsupervised machine learning to automatically identify different photoreaction channels and critical molecular motions from on -thefly TSH dynamics simulations, effectively addressing challenges like characterizing the ring distortion for keto iso cytosine. Lan and co-worker's recent work highlights the growing potential of integrating unsupervised machine learning methods into NAMD simulation analysis, emphasizing the need for interdisciplinary collaboration and the future development of automated "black-box" tools to enhance efficiency and insight into nonadiabatic dynamics. Additionally, some progress has been made in applying unsupervised machine learning algorithms to analyze the nonadiabatic dynamics of solid-state systems. Prezhdo et al. applied unsupervised machine learning to analyze correlations between structural and electronic properties of CsPbI₃ perovskite, establishing key geometric features and motions that govern charge carrier dynamics in this widely studied solar cell material. Despite the emergence of such advanced techniques to address the challenges of analyzing high -dimensional data in this fast-paced field, these methods often function independently, requiring distinct approaches for each type of data generation and clustering. This separation can complicate the practical analysis and interpretation of results.