The Quantum Theory of Atoms in Molecules (QTAIM) provides
an intuitive,
yet physically sound, strategy to determine the partial charges of
any chemical system relying on the topology induced by the electron
density ρ(r) . In a previous work [J. Chem.
Phys.2022,156, 014112], we
introduced a machine learning (ML) model for the computation of QTAIM
charges of C, H, O, and N atoms at a fraction of the conventional
computational cost. Unfortunately, the independent nature of the atomistic
predictions implies that the raw atomic charges may not necessarily
reconstruct the exact molecular charge, limiting the applicability
of the latter in the chemistry realm. Trying to solve such an inconvenience,
we introduce NNAIMGUI, a user-friendly code which combines the inferring
abilities of ML with an equilibration strategy to afford adequately
behaved partial charges. The performance of this approach is put to
the test in a variety of scenarios including interpolation and extrapolation
regimes (e.g chemical reactions) as well as large systems. The results
of this work prove that the equilibrated charges retain the chemically
accurate behavior reproduced by the ML models. Furthermore, NNAIMGUI
is a fully flexible architecture allowing users to train and use tailor-made
models targeted at any atomic property of choice. In this way, the
GUI-interfaced code, equipped with visualization utilities, makes
the computation of real-space atomic properties much more appealing
and intuitive, paving the way toward the extension of QTAIM related
descriptors beyond the theoretical chemistry community.