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DeePKS-kit is a program to generate accurate energy functionals for quantum chemistry systems, for both perturbative scheme (DeePHF) and self-consistent scheme (DeePKS).
The main features of deepmodeling/deepks-kit are: Density Functional Theory.
Open-source alternatives to deepmodeling/deepks-kit include: acesuit/acehamiltonians.jl — The ACEhamiltonians package is a Julia package that provides tools for constructing, fitting, and predicting… aiforgreatgood/charge3net — Official implementation of ChargeE3Net, introduced in Higher-Order Equivariant Neural Networks for Charge Density… andreagrisafi/salted — SALTED: Symmetry-Adapted Learning of Three-dimensional Electron Densities This repository contains an implementation… deepmodeling/dftio — dftio is to assist machine learning communities to transcript DFT output into a format that is easy to read or used by… divelab/airs — [license-image]:https://img.shields.io/badge/license-GPL3.0-green.svg [license-url]:https://github.com/divelab/AIRS/blo… google-research/google-research — This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum…
Official implementation of ChargeE3Net, introduced in Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials.
SALTED: Symmetry-Adapted Learning of Three-dimensional Electron Densities This repository contains an implementation of symmetry-adapted Gaussian Process Regression suitable to perform equivariant learning and prediction of the electron density of molecular and condensed-phase systems, together…
dftio is to assist machine learning communities to transcript DFT output into a format that is easy to read or used by machine learning models.
The ACEhamiltonians package is a Julia package that provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-state systems. It is based on the atomic cluster expansion (ACE) approach and the associated ACEsuit package. The ACEhamiltonians…