SchNet4AIM
About
SchNet4AIM is a code designed to train SchNet deep-learning models on atomic (1-body) and pairwise (2-body) properties formulated within the Quantum Theory of Atoms in Molecules (QTAIM). It is built as a targeted modification of SchNetPack, retaining only the components relevant for 1p/2p property training.
Key features:
- Train on atomic (charges, energies, volumes) or pairwise (delocalization indices, IQA interaction energies) QTAIM properties.
- Supports JSON and ASE-SQLite database formats.
- Runs on CPU and GPU (GPU recommended for speed).
Installation
git clone https://github.com/QTCOVI/SchNet4AIM.git
cd SchNet4AIM
pip install -r requirements.txt
Links
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.