Title: Unsupervised learning approach to quantum wavepacket dynamics from coupled temporal-spatial correlations

Abstract: Understanding complex quantum dynamics in realistic materials requires insight into the underlying correlations dominating the interactions between the participating particles. Due to the wealth of information involved in these processes, applying artificial intelligence methods is compelling. Yet, unsupervised data-driven approaches typically focus on maximal variations of the individual components, rather than considering the correlations between them. Here we present an approach that recognizes correlation patterns to explore convoluted dynamical processes. Our scheme is using singular value decomposition (SVD) to extract dynamical features, unveiling the internal temporal-spatial interrelations that generate the dynamical mechanisms. We apply our approach to study light-induced wavepacket propagation in organic crystals, of interest for applications in material based quantum computing and quantum information science. We show how transformation from the input momentum and time coordinates onto a new correlation-induced coordinate space allows direct recognition of the relaxation and dephasing components dominating the dynamics and demonstrate their dependence on the initial pulse shape. Entanglement of the dynamical features is suggested as a pathway to reproduce the information required for further explainability of these mechanisms. Our method offers a route for elucidating complex dynamical processes using unsupervised AI-based analysis in multi-component systems.