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knc6/README.md

Hi there 👋

Kamal Choudhary is

  • a staff scientist at National Institute of Standards and Technology (NIST), MD, USA.
  • the CEO and founder of a small business DeepMaterials LLC (https://www.deepmaterials.org/)
  • affiliate professor at the George Mason University
  • adjunct professor at the Johns Hopkins University
  • associate editor of the Journal Nature:NPJ Computational Materials (https://www.nature.com/npjcompumats/editors) and editorial board member of Nature:Scientific Data.
  • editorial board member of Materials Today Communications, PRX Energy journals

My research interests are focused on atomistic materials design using classical, quantum, and machine learning methods. In particular, I have developed JARVIS database and tools (https://jarvis.nist.gov/) that hosts publicly available datasets for millions of material properties.

Contributions to projects:

Name Description Details Conda Package PyPi Package
knc6/material explorer_examples ChatGPT based Material Science Assistant for materials data and simulations.
usnistgov/jarvis JARVIS-Tools: An open-source software package for data-driven atomistic materials design 📚 📦 📦
usnistgov/alignn ALIGNN: Atomistic Line Graph Neural Network and force-field 📚 📦 📦
usnistgov/jarvis_leaderboard JARVIS-Leaderboard: Explore State-of-the-Art Materials Design Methods and Reproducible Benchmarks 📚 📦 📦
usnistgov/atomgpt AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design 📚
usnistgov/chemnlp ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data 📚 📦 📦
usnistgov/atomvision AtomVision: Deep learning framework for atomistic image data 📚 📦
usnistgov/atomqc AtomQC: Atomistic Calculations on Quantum Computers 📚 📦
JARVIS-Materials-Design/jarvis-tools-notebooks A Google-Colab Notebook Collection for Materials Design 📚
deepmaterials/dlmatreview Repository for links to software packages and databases used in deep-learning applications for materials science 📚
deepmaterials/slmat ServerLess Materials Design Toolkit 📚
usnistgov/tb3py TB3Py: Two- and three-body tight-binding calculations for materials 📚 📦 📦
usnistgov/intermat InterMat: Interface materials design toolkit 📚 📦 📦
usnistgov/defectmat DefectMat: Defect materials design toolkit 📚
usnistgov/chipsff Evaluation of universal machine learning force-fields 📚 📦
SciVedanta A collection of YouTube videos on Vedanta philosophy 📚
eesociety Encouraging Excellence Society 📚

Here are some links that might interest you:

 knc6

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  1. usnistgov/jarvis usnistgov/jarvis Public

    JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications: https://scholar.google.com/citations?user=3w6ej94AAAAJ https://www.youtube.com/watch?v=2-XHeC…

    Python 329 129

  2. usnistgov/alignn usnistgov/alignn Public

    Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en https://www.youtube.com/watch?v=WYePjZMzx3M

    Python 251 90

  3. JARVIS-Materials-Design/jarvis-tools-notebooks JARVIS-Materials-Design/jarvis-tools-notebooks Public

    A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/

    Jupyter Notebook 74 32

  4. usnistgov/jarvis_leaderboard usnistgov/jarvis_leaderboard Public

    A large scale benchmark of materials design methods: https://www.nature.com/articles/s41524-024-01259-w

    Python 63 45

  5. usnistgov/atomqc usnistgov/atomqc Public

    Atomistic Calculations on Quantum Computers

    Python 15 10

  6. deepmaterials/dlmatreview deepmaterials/dlmatreview Public

    Repository for links to software packages and databases used in deep-learning applications for materials science

    TeX 136 27