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This study investigates the use of machine learning (ML) to correct theenthalpy of formation (Hf) from two separate DFT functionals, PBE and SCAN, tothe experimental Hf across 1011 solid-state compounds. The ML model uses a setof 25 properties that characterize the electronic structure as calculated usingPBE and SCAN. The ML model significantly decreases the error in PBE-calculatedHf values from an mean absolute error (MAE) of 195 meV/atom to an MAE = 80meV/atom when compared to the experiment. For PBE, the PDP+GAM analysis showscompounds with a high ionicity (I), i.e., I>0.22, have errors in Hf that aretwice as large as compounds having I < 0.22 (246 meV/atom compared to 113meV/atom). Conversely, no analogous trend is observed for SCAN-calculated Hfs,which explains why the ML model for PBE can more easily correct the systematicerror in calculated Hfs for PBE but not for SCAN. Although the literaturesuggests PBE is reliable for intermetallics but less so for oxides and halides,our analysis reveals intermetallics pose a challenge for PBE only when thecharge transfer is significant (I >0.22). Meanwhile, oxides and halides may bedescribed accurately by PBE for systems in which charge transfer is relativelylow (I < 0.22).

Interpretable machine learning to understand the performance of semi local density functionals for materials thermochemistry