The NMRlipids Databank-API delivers programmatic access to MD simulation data that can be used as training set for diverse data-driven and machine learning applications that predict membrane properties. Such applications could be analogous to AlphaFold 3 and other tools that predict protein structures from their sequence using artificial intelligence. This is demonstrated here by building ML models to predict multi-component membrane properties. Furthermore, the analysis of cholesterol flip-flop events (Fig. ) and water permeation through membranes (Fig. ) demonstrate how a large amount of accessible simulation data in terms of quantity (e.g., simulation length and number of conformations) and content (e.g., lipid compositions and ion concentrations) enable analyses of rare phenomena that are beyond the current possibilities for a single research group. Such analyses also pave the way for applications of MD simulations in new fields, as demonstrated here by analysing an essential parameter in pharmacokinetic modeling and MRI imaging: the anisotropic diffusion of water in membrane systems (Fig. ). These possibilities are particularly valuable for scientists who do not typically have access to large-scale MD simulation data.