<p></p><p>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 machine learning
framework toward open-shell anions and cations. We introduce AIMNet-NSE (Neural
Spin Equilibration) architecture, which being properly trained, could predict
atomic and molecular properties for an arbitrary combination of molecular
charge and spin multiplicity. This model explores a new dimension of
transferability by adding the charge-spin space. The AIMNet-NSE model is
capable of reproducing reference QM energies for cations, neutrals, and anions
with errors of about 2-3 kcal/mol, compared to the reference QM simulations.
The spin-charges have errors ~0.01 electrons for small organic molecules containing
nine chemical elements {H, C, N, O, F, Si, P, S and Cl}. <a>The
AIMNet-NSE model allows 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 with
a speed up to 10<sup>4</sup> molecules per second on a single modern GPU.</a> We
show that these descriptors, along with learned atomic representations, could
be used to model chemical reactivity through an example of regioselectivity in
electrophilic aromatic substitution reactions.</p><p></p>
Performance and Cost Assessment of Machine Learning Interatomic Potentials
