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Official implementation of ChargeE3Net, introduced in Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials.
nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset
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…
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…
[license-image]:https://img.shields.io/badge/license-GPL3.0-green.svg [license-url]:https://github.com/divelab/AIRS/blob/main/LICENSE [contributing-image]:https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat
The main features of divelab/airs are: Density Functional Theory, Datasets and Databases, General Tools, Community Resources.
Projects with overlapping indexed features include: aiforgreatgood/charge3net — Official implementation of ChargeE3Net, introduced in Higher-Order Equivariant Neural Networks for Charge Density… airi-institute/nabladft — nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset. andreagrisafi/salted — SALTED: Symmetry-Adapted Learning of Three-dimensional Electron Densities This repository contains an implementation… datamol-io/molfeat — molfeat - the hub for all your molecular featurizers Docs | Homepage. dbt-labs/docs.getdbt.com — We use docusaurus to power docs.getdbt.com. acesuit/acehamiltonians.jl — The ACEhamiltonians package is a Julia package that provides tools for constructing, fitting, and predicting…