  Machine learning materials properties measured by experiments is valuable yet
difficult due to the limited amount of experimental data. In this work, we use
a multi-fidelity random forest model to learn the experimental formation
enthalpy of materials with prediction accuracy higher than the empirically
corrected PBE functional (PBEfe) and meta-GGA functional (SCAN), and it
outperforms the hotly studied deep neural-network based representation learning
and transfer learning. We then use the model to calibrate the DFT formation
enthalpy in the Materials Project database, and discover materials with
underestimated stability. The multi-fidelity model is also used as a
data-mining approach to find how DFT deviates from experiments by the
explaining the model output.
