From the model development side, accurately accounting for long-range dependence (LRD) in both spatial and temporal dimensions is one of the crucial yet unmet challenges. LRD manifests in the slowly decaying dependence structure, such that the Markov assumption is no longer a valid approximation. Current approaches using deep-state space models are limited to video frame classification and generation, further improvements on both spatial and temporal LRD, computational efficiency, and the accommodation of graph-structured data will be fitting for the demands in materials data integration.