IFC Fitting Approach: We compare IFC fitting methods primarily focusing on three criteria: the error (RMSE), the cost (HiPhive runtime), and the convergence (% Converged). We applied each set of cutoffs for the same suite of 9 cubic structures as in Figure in a 20 Å lattice and measured the time of the HiPhive fitting. If it can not be finished within our wall time (2 hours here, which should be rather sufficient based on our experience), this single fitting is regarded as unconverged, and we compute the ratio of converged HiPhive fitting operations among 9 (materials) × 27 (cutoff sets) total runs for each fitting approach. From our benchmarking results depicted in Figure , least squares (LS) is the fastest method but it suffers from low accuracy and high uncertainty, evidenced by a high RMSE and large error range. This is unsurprising since IFC matrix is known to be sparse and near-sighted, and least-squares cannot favor sparse recovery . All other methods are capable of favoring sparse recovery and offer obviously better RMSE than the least-squares. Among them, we find that 26 % of the cases using the elastic net regularization and 14 % of the cases using the least absolute shrinkage and selection operator (LASSO) fail to converge, which are substantial proportions. Recursive feature elimination (RFE) shows very similar RMSE and speed to LASSO (see Table ) but with essentially 100 % convergence. Automatic relevance determination regression (ARDR) has a bit higher RMSE than RFE. All things considered, we select RFE as the default method in the workflow (see Table ), but the user may override this default with another method facilitated by our integration of importing methods from Scikit-learn . We note that the use of RFE has also been recommended by the HiPhive developers .