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    34 repositories

    • matgl

      Public
      Graph deep learning library for materials
      learninggraphdeepmaterials-sciencematerials-informatics
      Python
      •
      BSD 3-Clause "New" or "Revised" License
      •66•290•2•3•Updated Dec 13, 2024Dec 13, 2024

    • monty

      Public
      This repository implements supplementary useful functions for Python that are not part of the standard library. Examples include useful utilities like transparent support for zipped files etc.
      Python
      •
      MIT License
      •50•75•5•4•Updated Dec 11, 2024Dec 11, 2024

    • maml

      Public
      Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.
      machine-learningdeep-learningmaterials-sciencespectroscopymachine-learning-force-fieldmaterials-discovery
      Jupyter Notebook
      •
      BSD 3-Clause "New" or "Revised" License
      •79•376•7•13•Updated Dec 10, 2024Dec 10, 2024

    • pymatgen-analysis-diffusion

      Public
      This add-on to pymatgen provides tools for analyzing diffusion in materials.
      Python
      •
      BSD 3-Clause "New" or "Revised" License
      •56•99•7•4•Updated Dec 2, 2024Dec 2, 2024

    • m3gnet

      Public
      Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.
      machine-learningneural-networkgraphmaterials-sciencedeep-learning
      Python
      •
      BSD 3-Clause "New" or "Revised" License
      •63•249•15•1•Updated Dec 2, 2024Dec 2, 2024

    • matpes

      Public
      Tools for Working with MatPES
      Python
      •
      BSD 3-Clause "New" or "Revised" License
      •0•1•0•0•Updated Nov 26, 2024Nov 26, 2024

    • nano266

      Public
      Repository for UCSD NANO 266 Quantum Mechanical Modelling of Materials
      TeX
      •
      BSD 3-Clause "New" or "Revised" License
      •21•20•0•0•Updated Nov 13, 2024Nov 13, 2024

    • matcalc

      Public
      A python library for calculating materials properties from the PES
      learningmaterialsmachinepropertiespes
      Python
      •
      BSD 3-Clause "New" or "Revised" License
      •13•67•1•4•Updated Nov 6, 2024Nov 6, 2024

    • flamyngo

      Public
      Flask frontend for MongoDB
      Python
      •
      BSD 3-Clause "New" or "Revised" License
      •7•15•0•4•Updated Nov 4, 2024Nov 4, 2024

    • matgenb

      Public
      Jupyter notebooks demonstrating the utilization of open-source codes for the study of materials science.
      Jupyter Notebook
      •
      BSD 3-Clause "New" or "Revised" License
      •146•233•2•1•Updated Jul 17, 2024Jul 17, 2024

    • matgenie

      Public
      Web interface to pymatgen
      Python
      •1•6•0•2•Updated Apr 8, 2024Apr 8, 2024

    • nano281

      Public
      Data Science for Materials Science
      Jupyter Notebook
      •
      BSD 3-Clause "New" or "Revised" License
      •26•59•0•1•Updated Feb 21, 2024Feb 21, 2024

    • miworkshop

      Public
      Jupyter Notebook
      •
      BSD 3-Clause "New" or "Revised" License
      •1•0•0•1•Updated Feb 21, 2024Feb 21, 2024

    • .github

      Public
      0•0•0•0•Updated Feb 6, 2024Feb 6, 2024

    • python_template

      Public template
      A template repository for setting up new python packages
      Python
      •
      Other
      •0•0•0•0•Updated Aug 2, 2023Aug 2, 2023

    • garnetdnn

      Public
      This repo implements a web application utilizing a deep neural network to predict the formation energies and stability of garnet crystals.
      Python
      •
      BSD 3-Clause "New" or "Revised" License
      •7•32•1•3•Updated May 1, 2023May 1, 2023

    • megnet

      Public archive
      Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
      machine-learningdeep-learningtensorflowkerasmaterials-sciencegraph-networks
      Jupyter Notebook
      •
      BSD 3-Clause "New" or "Revised" License
      •158•508•17•4•Updated Apr 27, 2023Apr 27, 2023

    • materialsvirtuallab.github.io

      Public
      A guide to the Materials Virtual Lab
      TeX
      •2•4•0•1•Updated Apr 12, 2023Apr 12, 2023

    • maml-apps-rfxas

      Public
      Jupyter Notebook
      •
      BSD 3-Clause "New" or "Revised" License
      •1•0•0•1•Updated Mar 25, 2023Mar 25, 2023

    • api

      Public
      New API client for the Materials Project
      Python
      •
      Other
      •43•0•0•10•Updated Mar 6, 2023Mar 6, 2023

    • Dash-User-Contributions

      Public
      Dash repo of user contributed docsets
      Python
      •852•0•0•2•Updated Mar 4, 2023Mar 4, 2023

    • crystals.ai

      Public
      CSS
      •0•0•0•0•Updated Dec 13, 2022Dec 13, 2022

    • enumlib

      Public
      Derivative structure enumeration library
      Roff
      •
      MIT License
      •35•0•0•0•Updated Nov 2, 2022Nov 2, 2022

    • pyhull

      Public
      Pyhull is a Python wrapper to Qhull (http://www.qhull.org/) for the computation of the convex hull, Delaunay triangulation and Voronoi diagram.
      C
      •
      MIT License
      •30•99•5•0•Updated Sep 26, 2022Sep 26, 2022

    • nano106

      Public
      Course materials for NANO 106 - Crystallography of Materials
      Jupyter Notebook
      •
      BSD 3-Clause "New" or "Revised" License
      •12•33•0•1•Updated Jun 21, 2022Jun 21, 2022

    • staged-recipes

      Public
      A place to submit conda recipes before they become fully fledged conda-forge feedstocks
      Python
      •
      BSD 3-Clause "New" or "Revised" License
      •5k•0•0•0•Updated Jun 17, 2022Jun 17, 2022

    • mlearn

      Public archive
      Benchmark Suite for Machine Learning Interatomic Potentials for Materials
      learningsciencematerialschemistrymachinepotentialsinteratomic
      Python
      •
      BSD 3-Clause "New" or "Revised" License
      •27•105•0•1•Updated Feb 22, 2022Feb 22, 2022

    • Data-driven-First-Principles-Methods-for-the-Study-and-Design-of-Alkali-Superionic-Conductors

      Public
      Jupyter notebooks and data for our Chemistry of Materials article "Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors"
      Jupyter Notebook
      •7•10•1•0•Updated Oct 1, 2020Oct 1, 2020

    • snap

      Public archive
      Repository for spectral neighbor analysis potential (SNAP) model development.
      machine-learningmaterials-scienceforce-field
      AMPL
      •
      BSD 3-Clause "New" or "Revised" License
      •19•37•1•0•Updated Jun 30, 2020Jun 30, 2020

    • veidt

      Public archive
      Veidt is a deep learning library for materials science.
      Python
      •
      BSD 3-Clause "New" or "Revised" License
      •9•18•0•0•Updated May 5, 2020May 5, 2020