Nevertheless, it is important to be cautious when employing ∆-learning. The primary sources of error stem not from capturing the dierence between the two functionals, but rather from potential gaps in the training dataset, which may fail to cover the necessary range of congurations. Consequently, predictions for the baseline semi-local functional can become relatively inaccurate, in the present case, if there is a signicant dierence between the volumes predicted by the hybrid and semi-local functional. The hybrid dataset should remedy this issue, since it spans a volume range covering ±10% around the DFT vdW strategy RPBE D3/zd base RPBE D3/BJ base revPBE D3/zd base revPBE D3/BJ base optPBE base & on-the-y SCAN on-the-y revPBE0 D3 experimental density.