iwaitingseven

iwaitingseven's Stars

• njszym/XRD-AutoAnalyzer

  Language:Python7216

• janosh/pymatviz

  A toolkit for visualizations in materials informatics.

  Language:Python15915

• materialsproject/reaction-network

  Reaction Network is a Python package for predicting likely inorganic chemical reaction pathways using graph theoretical methods. Project led by @mattmcdermott (formerly at Berkeley Lab).

  Language:Python8216

• QuantumMisaka/ATST-Tools

  Advanced ASE Transition State Tools for ABACUS and Deep-Potential

  Language:Python156

• pierre-24/cp2k-basis

  A library to parse CP2K basis sets and GTH pseudopotentials, which also comes with a webserver to find out and select a basis set and its matching pseudopotential for your CP2K calculations.

  Language:Python31

• PacktWorkshops/The-Deep-Learning-with-PyTorch-Workshop

  Language:Jupyter Notebook3141

• dair-ai/ml-visuals

  🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

  13.2k1.4k

• materialsvirtuallab/matgl

  Graph deep learning library for materials

  Language:Python25659

• lrcfmd/ipcsp

  Integer Programming encoding for Crystal Structure Prediction with classic and quantum computing bindings

  Language:Python4215

• dembart/BVlain

  The Bond valence site energy calculator

  Language:Python91

• pybamm-team/PyBaMM

  Fast and flexible physics-based battery models in Python

  Language:Python1.1k533

• pybamm-team/pybamm-supporting-material

  Supporting materials for PyBaMM

  Language:Jupyter Notebook71

• pybamm-team/liionpack

  A battery pack simulation tool that uses the PyBaMM framework

  Language:Python8527

• IntelLabs/matsciml

  Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery supporting widely used materials science datasets, and built on top of PyTorch Lightning, the Deep Graph Library, and PyTorch Geometric.

  Language:Python14320

• sedaoturak/data-resources-for-materials-science

  A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.

  25542

• JaGeo/LobsterPy

  Package to perform automatic bonding analysis with the program Lobster in the field of computational materials science and quantum chemistry

  Language:Python7428

• deepchem/deepchem

  Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

  Language:Python5.4k1.7k

• txie-93/cgcnn

  Crystal graph convolutional neural networks for predicting material properties.

  Language:Python639307

• hackingmaterials/amset

  Electronic transport properties from first-principles calculations

  Language:Python13245

• hackingmaterials/atomate

  atomate is a powerful software for computational materials science and contains pre-built workflows.

  Language:Python241173

• hackingmaterials/matminer

  Data mining for materials science

  Language:HTML471193

• hackingmaterials/automatminer

  An automatic engine for predicting materials properties.

  Language:Python13550

• cp2k/cp2k-input-tools

  Fully validating pure-python CP2K input file tools including preprocessing capabilities

  Language:Python4610

• gVallverdu/myScripts

  Script library mainly about chemical physics.

  Language:Python2323

• aiidateam/aiida-cp2k

  The CP2K plugin for the AiiDA workflow and provenance engine.

  Language:Python2228

• asche1/PyCrystalField

  Code to calculate the crystal field Hamiltonian of magnetic ions.

  Language:Jupyter Notebook4918