generally provide a more accurate and reliable description of the magnetic systems compared to other functionals. In addition, SCAN, as meta-GGA functional, shows similar accuracy as many hybrid functionals, especially for the prediction of ΔEspin, where the MAE and MRE are 0.36 eV and -11%. This provides a significant computational advantage in optimizing the magnetic state for large systems, especially for high-spin states, as meta-GGA functionals are generally less computationally expensive. However, some functionals mistake the sign of J and ΔEspin, leading to an incorrect prediction of the ground state. For example, PBE will reverse the sign of the J for all the TRI(B) series molecules and the sign of ΔEspin for all the TAM and most of the TOT molecules (Table ). Although ωB97XD and ωB97X-V correctly predict the signs of J and ΔEspin for all the molecules in the dataset, the MRE of ΔEspin are 104% and 113%, which is about three times larger than other hybrid functionals, indicating severe overestimation of electron correlation. The 〈𝑆 2 〉 HS and 〈𝑆 2 〉 LS of all these functionals for the molecules in the dataset are shown in Table . The spin densities of all the dimer molecules calculated by MN15 functional are shown in Figures S27-S50 Optimizing the range-separation parameter ω, rather than using a single value for all the systems, may lead to a more accurate description of magnetic states, as this has been demonstrated to enhance the prediction of the fundamental gaps and the chargetransfer excitations in organic assemblies and polymers. Furthermore, previous research has demonstrated that LC-ωPBE improves the description of magnetic coupling in transition-metal oxides compared to B3LYP. In view of the direct impact on the electron correlations, we calculated J and ΔEspin with long-range (LC) corrected hybrid functional, LC-ωPBE, and employ different values of ω based on the building monomers, where ω=0.20 for TRI, ω=0.15 for TOT, TAM and PLY, ω=0.10