NNAIMQ

Jan 1, 2024
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About

NNAIMQ predicts QTAIM (Bader) partial charges for C, H, O and N atoms in neutral, singlet-spin gas-phase organic and biological molecules. It comprises four Artificial Neural Networks (one per element) fitted to high-quality quantum chemical data.

Key features:

  • High-accuracy QTAIM charges without running a full topological analysis.
  • Supports standard .xyz geometry files as input.
  • Compatible with x86-64 and ARM (Apple M1) processors.

Requirements

  • Python ≥ 3.7.3
  • keras, matplotlib, numpy, pandas, seaborn, tensorflow

Usage

cd code/
python nnaimq.py input

where input is a plain-text file listing the .xyz geometry files to process.

Machine Learning QTAIM Atomic Charges 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.