Machine Learning & Neural Network Potentials

Overview

Machine learning is transforming computational chemistry, and we are harnessing it in two complementary ways:

IQA-Informed Neural Network Potentials

Classical machine-learned interatomic potentials (NNPs) are trained on total energies and forces, but lack chemical interpretability. We develop NNPs informed by IQA energy components (self-energies and interaction energies), resulting in potentials that:

• Decompose into physically meaningful atomic and pairwise contributions.
• Transfer more reliably to out-of-distribution chemical environments.
• Naturally encode the correct physics of bonding interactions.

Topological Descriptors as ML Features

QTAIM atomic properties and IQA energy components serve as physically motivated features for machine learning models targeting molecular properties, reaction barriers, and drug–target binding affinities.

Deep Learning for Electron Density

We explore the use of deep learning models to predict electron densities directly, enabling rapid computation of topological properties for large molecular datasets.

Codes & Tools

Our ML work builds on open-source frameworks (PyTorch, JAX) and is integrated with our in-house topological analysis codes.