To demonstrate the usage of the NMRlipids databank to construct machine learning (ML) models that predict membrane properties, we trained a model that predicts area per lipids and thicknesses of membranes with diverse compositions. First we used randomly selected 80% of the databank simulations to choose the hyperparameters for, and optimize, a set of machine learning models with the goal of predicting the area per lipid from the membrane composition. Examples of data obtained using NMRlipids Databank. A Area per lipid of POPC and POPE lipid bilayers predicted by different force fields at 310 K in simulations that are available in the NMRlipids Databank. The data points from the best-performing simulations, based on rankings in panels B and C, are surrounded by black circles. B Best POPC simulations ranked based on the sn-1 acyl chain order parameter quality (P sn1 ). Also sn-2 acyl chain (P sn2 ), headgroup (P hg ) and total (P total ) order parameter qualities, form factor quality (FF q ), and relative equilibration time for conformations (τ rel ) are shown. Note that the best possible order parameter quality is one, while the best possible form factor quality is zero. C Best POPE simulations ranked based on the sn-1 acyl chain order parameter quality. D-F Direct comparison against experimental (NMR order parameters and X-ray scattering ) data exemplified for a simulation with the best overall order parameter quality (D), the best quality for POPE lipid (E), and the headgroup quality for POPE (F). G Scatter plots and Pearson correlation coefficients, r, for the membrane area per lipid, thickness, first minimum of X-ray scattering form factor and average order parameter of the sn-1 acyl chain extracted from the NMRlipids Databank. All correlation coefficients have p-value below 0.001. For more correlations see Fig. .