QTAIM

A Python-interfaced neural network model for the rapid prediction of QTAIM atomic charges of C, H, O and N atoms in gas-phase organic and biological molecules.

A high-performance Fortran code for topological analysis of electron densities and pair densities, implementing QTAIM, IQA, electron localisation function (ELF), and delocalization indices.

We use and develop the mathematical framework of Quantum Chemical Topology (QCT), including the Quantum Theory of Atoms in Molecules (QTAIM), to analyse electron densities and provide chemically meaningful partitioning of molecular properties.

A deep learning code based on the SchNet architecture for training models on atomic (1-body) and pairwise (2-body) QTAIM properties. Supports CPU and GPU execution.

A graphical user interface for the prediction and visualisation of QTAIM atomic properties using feed-forward neural network models. Includes the built-in NNAIMQ model for Bader charges.