11 open-source projects similar to quantumlab-zy/hamgnn, ranked by shared indexed features. Tags may describe platforms or build tools rather than the same primary purpose. Check each project’s use case, license, and deployment requirements before treating it as a replacement.
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…
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…
DeePKS-kit is a program to generate accurate energy functionals for quantum chemistry systems, for both perturbative scheme (DeePHF) and self-consistent scheme (DeePKS).
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.
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
This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed
scdp is a codebase for training charge density prediction models described in the paper:
experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental) Q-stack
MALA (Materials Learning Algorithms) is a data-driven framework to generate surrogate models of density functional theory calculations based on machine learning. Its purpose is to enable multiscale modeling by bypassing computationally expensive steps in state-of-the-art density functional…
CiderPress: Machine Learned Exchange-Correlation Functionals