For a simple test case, we generate a database of 284 random X FCC configurations with an equimolar AlLiMgSnZn stoichiometry (the synthesis target of Ref. [10]), DFT optimize (Supplementary Section 1) their geometries to obtain X DFT and E f,DFT , and map X DFT back to X FCC . Figure summarizes both X DFT and X FCC models' test set performance on a 80/10/10 train/validation/test split. Henceforth referred to as Model 1, the GNN trained on X FCC inputs still predicts Êf with nearly identical accuracy as one trained on X DFT , with a mean absolute error (MAE) of 3.1 vs 3.6 meV/atom. Since X DFT unambiguously maps to X FCC despite local distortions, we can model the relaxed formation enthalpies and perform high-throughput configurational screening by using X FCC as a trivial-to-generate input.