deepmodeling/DeePTB

Universal machine-learning models for advanced atomistic simulations

Python library to compute properties of quantum tight binding models, including topological, electronic and magnetic properties and including the effect of many-body interactions.

SchNetPack - Deep Neural Networks for Atomistic Systems

Graph deep learning library for materials

Electronic structure Python package for post analysis and large scale tight-binding DFT/NEGF calculations

An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations

Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov

A package for tight-binding model of magnetic and non-magnetic materials

An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix