The two main paradigms in materials science that benefit from advancements in spatiotemporal models are: (i) Self-driving (or autonomous) laboratories . They deploy robots and machine learning-driven sequential decision-making from streaming data to search through high-dimensional parameter spaces (such as process, composition, and property parameters) for materials optimization. A growing number of them are installed at large-scale research facilities such as X-ray or neutron sources or in regular research institutions for organic and inorganic synthesis. (ii) Combined large-scale atomistic simulation and video-mode recording of time-resolved experiments . Here both the simulation and the data analysis may be powered by machine learning algorithms, while data integration between the two modalities through a spatiotemporal model is needed to obtain experimentally validated physical parameters. Both of these two paradigms will benefit from the following developments: