The number of features saved after SIS was set to 20. SISSO then uses 0 -norm minimization as a sparsifying operator (SO) to The computational cost of SISSO-generated models varies depending on the number and type of primary features they contain. The cost of each model was evaluated by summing over the costs of all the primary features included in it. The cost of features that appear in the model more than once was counted only once. The computer time required to calculate the single molecule PBE gap, Gap S , was assigned a value of 1 cost unit and the cost of other features is tabulated in the SI as multiples of that unit. The cost of all the primary features has been updated from the values given in Ref. to account for new developments in the latest version of FHI-aims. In particular, the MBD calculation has become significantly more efficient than in older versions of the code. The cost was averaged over the 10 structures in the validation set, rather than picking one system of average size, as in Ref. . Figure shows a Pareto chart of the accuracy vs. the computational cost of the SISSO models considered here. The "train" RMSE is calculated for the training set of 91 structures. The "test" RMSE is calculated for the 10 withheld materials, which were excluded from the LCV. The best balance of cost and accuracy is provided by the M 4,2 model: