knc6 Goto Github PK

alignn

Atomistic Line Graph Neural Network

api-examples

Example usage of Exabyte.io platform through its RESTful API: programmatically create materials and modeling workflows, execute simulations on the cloud, analyze data and build machine learning models

atomqc

Atomistic Calculations on Quantum Computers

atomvision

Deep learning framework for atomistic image data

cdvae

An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]

cgcnn

Crystal graph convolutional neural networks for predicting material properties.

chemnlp-1

ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data

core_main_app

djangoapp

The polls app from the official Django tutorial, that demonstrates how to build data-driven Python apps in Azure App Service.

gasp-python

Genetic algorithm for structure and phase prediction interfaced to GULP, LAMMPS and VASP.

github-django-actions

gnn-tutorial-aps-march-2023

These are the slides associated with the GNN tutorial at the APS March Meeting

heroku-django-deploy

A simple github action that automatically deploys Django app to heroku

jarvis

JARVIS-Tools: an open-source software package for data-driven atomistic materials design

jarvis-ff

This project contains the data for evaluation of interatomic potentials/force-fields (used in Moecular-dynamics and Monte-carlo simulations). LAMMPS calculation were done using MPinterface code (https://github.com/JARVIS-Unifies/JARVIS-FF) and in.elastic script in LAMMPS/examples/ELASTIC folder (https://github.com/lammps/lammps/tree/master/examples/ELASTIC) on the structures downloaded from materials project (MP) using REST API (https://www.materialsproject.org/).Force-fields were downloaded from interatomic potential repository project(http://www.ctcms.nist.gov/potentials/) and LAMMPS (https://github.com/lammps/lammps/tree/master/potentials). The interactive plot was made with Bokeh (http://bokeh.pydata.org/en/0.10.0/docs/gallery/periodic.html). Please note that the starting lattice parameters were taken from density functional theory (DFT) and not from experiments. Generic minimization parameters were chosen for LAMMPS run rather than testing them for each individual case such as energy convergence criterion and so on. Hence, there are chances that the calculation gets trapped in a local energy minima. Please read carefully the assumptions taken during calculations in the in.elastic script and use the data at your own risk !

jarvis-tools

jarvis-tools-feedstock

A conda-smithy repository for jarvis-tools.

jarvis-tools-notebooks

jarvis_leaderboard

Explore State-of-the-Art Materials Design Methods: https://www.nature.com/articles/s41524-024-01259-w

knc6

matbench

Matbench: Benchmarks for materials science property prediction

matbench-discovery

An evaluation framework for machine learning models simulating high-throughput materials discovery.

mdcs-api-tools

mdcs1

optimade-python-tools

Tools for implementing and consuming OPTIMADE APIs in Python

prismatic

C++/CUDA package for parallelized simulation of image formation in Scanning Transmission Electron Microscopy (STEM) using the PRISM and multislice algorithms

providers

This repository hosts the providers.json file for OPTIMADE that lists reserved database-specific prefixes and URLs to the index databases of all database providers that participate in the OPTIMADE network

pymatgen

Python Materials Genomics (pymatgen) is a robust materials analysis code that defines core object representations for structures and molecules with support for many electronic structure codes. It is currently the core analysis code powering the Materials Project.

qiskit-community-tutorials

A collection of Jupyter notebooks developed by the community showing how to use Qiskit

qiskit-nature

Quantum Nature