Machine learning (ML) has emerged as a powerful tool to complement ab initio calculations and accelerate chemical discovery, especially with ML models trained on large sets of experimental data showing comparable accuracy to results from DFT. ML models have found applications in orbital-free DFT, accelerating molecular dynamics simulations, heterogeneous catalyst design, materials discovery, force fields, potential energy surface representations, and many-body expansions. ML models have also been used extensively to predict band gaps, excited state properties, redox potentials, 94,95 electron and spin densities, and the spin splitting energies 102 highly relevant to predicting SCO behavior. Prior studies 103,104 have also leveraged ML to describe magnetism in materials, which is particularly challenging due to its strong dependence on local environments. One approach 103 employed spin-dependent atom-centered symmetry functions as a type of descriptor for a highdimensional neural network potential while another 104 built a Euclidean equivariant neural network that preserved the crystallographic symmetry.