In 2019, Zubatyuk et al., developed AIMNet , a modular and chemically inspired deep neural network potential. AIMNet uses message passing and multitarget training to learn multiple modalities of the state of the atom in a molecular system. Later, the AIMNet architecture was extended with a Neural Spin-Charge Equilibration (NSE) block inside the neural network. The resulting AIMNet-NSE architecture can learn a transferable potential for molecular systems in arbitrary spin and charge states.