On the Inclusion of Charge and Spin States in Cartesian Tensor Neural Network Potentials

Guillem Simeon, Antonio Mirarchi, Raúl P. Peláez, Raimondas Galvelis, Gianni De Fabritiis

Abstract: 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.