The problem with option A is that, no matter how accurate a faithful DFT surrogate is, the faithful DFT surrogate will always become less accurate when it meets new data samples that it has not encountered during its training. The accuracy measures are highly dependent on the specific test set that was chosen for the ML task. That is, scoring an MAE as low as 10 meV for E f prediction will not guarantee that the same model will produce the same MAE for more diverse and larger test sets. Option B would be a more sensible objective. This means that the accuracy of ML models must be limited by the accuracy DFT against experimental data, or against higher rungs along the "Jacob ladder" of chemical accuracy. The most popular DFT exchange-correlation potential, GGA PBE, scores an MAE of the bandgap prediction error of GGA PBE is 1.1 eV. Hence, aiming at beating the baseline value of 0.16 eV on Matbench will be pointless. It would be more worthwhile to train ML models against experimental data.