On the Inclusion of Charge and Spin States in Cartesian Tensor Neural
  Network Potentials
Guillem Simeon Antonio Mirarchi Raul P. Pelaez Raimondas Galvelis Gianni De Fabritiis
In this letter, we present an extension to TensorNet, a state-of-the-art
equivariant Cartesian tensor neural network potential, allowing it to handle
charged molecules and spin states without architectural changes or increased
costs. By incorporating these attributes, we address input degeneracy issues,
enhancing the model's predictive accuracy across diverse chemical systems. This
advancement significantly broadens TensorNet's applicability, maintaining its
efficiency and accuracy.