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.

翻译：本文提出对TensorNet（一种先进的等变笛卡尔张量神经网络势）的扩展，使其无需修改架构或增加计算成本即可处理带电分子和自旋状态。通过纳入这些属性，我们解决了输入简并性问题，提升了模型在不同化学系统中的预测精度。该发展显著扩展了TensorNet的适用性，同时保持了其效率与准确性。