Deep neural networks for density functional theory Hamiltonian.

nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset

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

Open-source library for analyzing the results produced by ABINIT

Differentiable Quantum Chemistry (only Differentiable Density Functional Theory and Hartree Fock at the moment)

GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation functionals using machine learning techniques.

Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.

Python package to analyse electron density & electrostatic potential grids

Optimized Norm-Conserving Vanderbilt Pseudopotential for Quantum Espresso in UPF format

Document and code of python and PySCF approach XYG3 type of density functional 2nd-derivative realization

In silico chemical library engine for high-accuracy chemical property prediction

Thermodynamic Phase Diagram Generator

Python tools for automating routine tasks encountered when running quantum chemistry computations.

Python tool to manipulate Gaussian cube files

Exchange correlation functionals translated from libxc to jax

A toolbox for quickly build inputs and analyze results of DFT codes

MESS: Modern Electronic Structure Simulations

A physics computational framework for python and ipython

Extended DeepH (xDeepH) method for magnetic materials.

Positive and Unlabeled Materials Machine Learning (pumml) is a code that uses semi-supervised machine learning to classify materials from only positive and unlabeled examples.