We will now present the ML2 model-a machinelearned correlation energy density based on the regularized MP2 proxy reference-which we obtain by mapping a set of pointwise features using neural networks (NNs) [going from the blue square to the cyan or maroon diamonds in Fig. ]. This mapping is illustrated in Fig. . In addition to the established features used in ML of DFAs -the reduced density gradient s(r), the reduced density Laplacian q(r), and the regularized kinetic energy variable α(r) from the r 2 SCAN DFA -we introduce Grimme's electronic temperature-dependent fractional occupation number weighted density (FOD) as the crucial feature (a full list of features is given in Sec. S5 in the SI). FOD differentiates strongly correlated and weakly correlated regions in molecules. While both e κMP2 c (r) and FOD provide insights into electronic correlation through the interaction between occupied and unoccupied orbitals, FOD is computationally much cheaper, making it an excellent feature for ML of the correlation energy density.