Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry. In particular, interatomic potentials derived with Machine Learning algorithms such as Deep Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. The applicability domain of DNN potentials is usually limited by the type of training data. As such, transferable models are aimed to be extensible in the description of chemical and conformational diversity of organic molecules. However, most DNN potentials, such as the AIMNet model we proposed previously, were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we extend our AIMNet framework toward open-shell anions and cations. This model explores a new dimension of transferability by adding the charge-spin space. The resulting AIMNet model is capable of reproducing reference QM energies for cations, neutrals and anions with errors of 4.1, 2.1, 2.8 kcal/mol, respectively, compared to the reference QM simulations. The spin-charges have errors 0.01-0.06 electrons for small organic molecules containing nine chemical elements {H, C, N, O, F, Si, P, S and Cl}. Thus the proposed AIMNet model allows researchers to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regionselectivity in electrophilic aromatic substitution reactions.

受物理学启发的人工智能（AI）处于分子建模和计算化学方法开发的最前沿。特别是，通过机器学习算法（例如深度神经网络（DNN））获得的原子间势能在传统上由经验力场主导的区域中实现了高保真量子力学（QM）方法的准确性，并允许执行大规模模拟。DNN电位的适用范围通常受训练数据类型的限制。因此，可转移模型旨在在描述有机分子的化学和构象多样性时具有可扩展性。但是，由于架构限制，大多数DNN电位（例如我们之前提出的AIMNet模型）已针对中性分子或闭壳离子进行了参数化。在这项工作中 我们将AIMNet框架扩展到开放式阴离子和阳离子。该模型通过增加电荷自旋空间探索了可转移性的新维度。与参考QM模拟相比，所得的AIMNet模型能够为误差分别为4.1、2.1、2.8 kcal / mol的阳离子，中性离子和阴离子复制参考QM能量。对于包含九种化学元素{H，C，N，O，F，Si，P，S和Cl}的有机小分子，自旋电荷的电子误差为0.01-0.06。因此，提出的AIMNet模型允许研究人员完全绕过QM计算，并得出电离势，电子亲和力和概念性密度泛函理论量，例如电负性，硬度和凝聚的Fukui函数。我们证明了这些描述符以及所学的原子表示，