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@materialyzeai

Materialyze.AI

Our mission is to accelerate the design and discovery of breakthrough materials through the integration of theory, experiments, and AI.

The Materialyze.AI Lab at the National University of Singapore is a materials AI group focused on the cross-disciplinary application of materials science, computer science and machine learning to accelerate materials design. We develop cutting-edge software frameworks for automation of calculations, sophisticated data infrastructure for large materials data, and state-of-the-art machine learning models with high predictive accuracy.

Vision

Our vision is to bring forth a transformative leap in the speed and scale of materials design through data science and AI.

Mission

  • We develop techniques that effectively integrate materials science, computer science and information science.
  • We apply cutting-edge computational and experimental techniques to gain novel and useful insights into materials design.
  • We build open AI-scale software and data infrastructure for materials science.

Values

Integrity

  • We practice integrity in all forms.
  • We are honest and fair to fellow group members and collaborators.
  • We have a zero-tolerance policy towards plagiarism and falsification of results.

Excellence

  • We strive for excellence in everything that we do.
  • We stand by the quality of our science.
  • We aim to develop scientists with great analytical, technical and communication skills.

Teamwork

  • We believe great teamwork is the key to great science.
  • We share and discuss ideas freely.
  • We strive to build great collaborations, both within and outside of the group.
  • We contribute actively to the materials science community.

Pinned Loading

  1. maml maml Public

    Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.

    Jupyter Notebook 447 94

  2. matgl matgl Public

    Graph deep learning library for materials

    Python 509 106

  3. pymatgen-analysis-diffusion pymatgen-analysis-diffusion Public

    This add-on to pymatgen provides tools for analyzing diffusion in materials.

    Python 133 57

  4. monty 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 83 49

  5. matcalc matcalc Public

    A python library for calculating materials properties from the PES

    Python 131 32

  6. matpes matpes Public

    A foundational potential energy dataset for materials

    Jupyter Notebook 51 4

Repositories

Showing 10 of 33 repositories
  • pymatgen-core Public

    Pymatgen Core Modules

    materialyzeai/pymatgen-core’s past year of commit activity
    HTML 0 0 0 0 Updated Feb 21, 2026
  • pymatgen-test-files Public

    Test Files for Pymatgen

    materialyzeai/pymatgen-test-files’s past year of commit activity
    Polar 0 MIT 0 0 0 Updated Feb 20, 2026
  • matgl Public

    Graph deep learning library for materials

    materialyzeai/matgl’s past year of commit activity
    Python 509 BSD-3-Clause 106 3 3 Updated Feb 20, 2026
  • matcalc Public

    A python library for calculating materials properties from the PES

    materialyzeai/matcalc’s past year of commit activity
    Python 131 BSD-3-Clause 32 6 6 Updated Feb 20, 2026
  • flamyngo Public

    Flask frontend for MongoDB

    materialyzeai/flamyngo’s past year of commit activity
    Python 15 BSD-3-Clause 7 0 5 Updated Feb 19, 2026
  • nano106 Public archive

    Course materials for NANO 106 - Crystallography of Materials

    materialyzeai/nano106’s past year of commit activity
    Jupyter Notebook 36 BSD-3-Clause 14 0 0 Updated Feb 19, 2026
  • miworkshop Public archive
    materialyzeai/miworkshop’s past year of commit activity
    Jupyter Notebook 0 BSD-3-Clause 1 0 0 Updated Feb 19, 2026
  • nano281 Public archive

    Data Science for Materials Science

    materialyzeai/nano281’s past year of commit activity
    Jupyter Notebook 66 BSD-3-Clause 29 0 0 Updated Feb 19, 2026
  • 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.

    materialyzeai/monty’s past year of commit activity
    Python 83 MIT 49 2 3 Updated Feb 18, 2026
  • maml Public

    Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.

    materialyzeai/maml’s past year of commit activity
    Jupyter Notebook 447 BSD-3-Clause 94 10 2 Updated Feb 14, 2026

People

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