NNAIMGUI

Jan 1, 2023
Go to Project Site

About

NNAIMGUI (M. Gallegos, University of Oviedo, 2023) is a code for the prediction and visualisation of atomic properties using feed-forward neural network (FFNN) models. It ships with the built-in NNAIMQ model for predicting QTAIM charges of gas-phase neutral singlet molecules containing C, H, O and N atoms, and supports user-supplied custom models for any atomic property of interest.

Key features:

  • Graphical user interface for non-expert users, plus command-line mode.
  • Built-in charge equilibration to enforce molecular electroneutrality (13 algorithms included).
  • Supports user-defined FFNN models for any atomic property.
  • Compatible with Linux and Windows.

Installation

pip install git+https://github.com/m-gallegos/NNAIMGUI.git

Citation

M. Gallegos et al., J. Chem. Inf. Model. (2023). https://doi.org/10.1021/acs.jcim.3c00597

Machine Learning QTAIM Atomic Charges GUI Neural Networks
Ángel Martín Pendás
Ángel Martín Pendás
Principal Investigator

Professor of Physical Chemistry at the University of Oviedo. Pioneer of orbital-invariant approaches to chemical bonding, including the Interacting Quantum Atoms (IQA) energy partition and topological electron population statistics.