After feature generation, SISSO performs linear regression to yield the model prediction, where each model is the scalar product of the SISSO-generated feature with a vector of fitted coefficients. Then, the models are ranked according to their prediction performance. Sure independence screening (SIS) is used to select optimal subspaces from the huge feature space. The number of features saved after SIS was set to 20. SISSO then uses ℓ 0 -norm minimization as a sparsifying operator (SO) to determine the sparse solution for each such subspace. For each combination of dimension and rung, 40 rounds of leave-10-out cross validation (LCV) were performed. In each round, 10 data points (out of the 91 points used for model training) were randomly selected and held out as an unseen validation set. The model with the lowest RMSE for the validation set was selected in each round. Finally, the model with the lowest RMSE for the combined training and validation data was selected out of the 40 models. This model is denoted as M Dim,Rung . A full account of the SISSO models for each target property is provided in the SI. For each model, the training RMSE was calculated for the whole training set with 91 structures and the test RMSE was calculated for the 10 withheld materials, which were excluded from the LCV.