DCT (discrete cosine transform) functions for pytorch

JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications: https://scholar.google.com/citations?user=3w6ej94AAAAJ https://www.youtube.com/watch?v=2-XHeC8gbeY

Deep neural networks for density functional theory Hamiltonian.

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

quacc is a flexible platform for computational materials science and quantum chemistry that is built for the big data era.

A Python library for electronic structure pre/post-processing

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

atomate2 is a library of computational materials science workflows

doped is a Python software for the generation, pre-/post-processing and analysis of defect supercell calculations, implementing the defect simulation workflow in an efficient, reproducible, user-friendly yet powerful and fully-customisable manner.

scalable molecular simulation

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

Defect structure-searching employing chemically-guided bond distortions

Fast Fourier transform in MicroPython's inline ARM assembler.

a python package for computing magnetic interaction parameters

Multi-NILM: Multi Label Non Intrusive Load Monitoring

ASH is a Python-based computational chemistry and QM/MM environment, primarily for molecular calculations in the gas phase, explicit solution, crystal or protein environment.

DeePTB: A deep learning package for tight-binding approach with ab initio accuracy.

A repository for the implementation of common workflow interfaces across materials-science codes and plugins

DBSTEP: DFT-based Steric Parameters - python-based tool to extract molecular shape and steric descriptors from essentially any structure format

Scientific Python package for solving Slater Koster tight-binding topological hamiltonian