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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.

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  1. maml maml Public

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

    Jupyter Notebook 453 96

  2. matgl matgl Public

    Graph deep learning library for materials

    Python 530 108

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

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

    Python 137 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 50

  5. matcalc matcalc Public

    A python library for calculating materials properties from the PES

    Python 136 35

  6. matpes matpes Public

    A foundational potential energy dataset for materials

    Jupyter Notebook 53 5

Repositories

Showing 10 of 32 repositories
  • matgenb Public

    Jupyter notebooks demonstrating the utilization of open-source codes for the study of materials science.

    materialyzeai/matgenb’s past year of commit activity
    Jupyter Notebook 277 BSD-3-Clause 156 3 1 Updated Apr 6, 2026
  • pymatgen-analysis-diffusion Public

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

    materialyzeai/pymatgen-analysis-diffusion’s past year of commit activity
    Python 137 BSD-3-Clause 57 8 3 Updated Apr 6, 2026
  • matgl Public

    Graph deep learning library for materials

    materialyzeai/matgl’s past year of commit activity
    Python 530 BSD-3-Clause 108 3 4 Updated Apr 6, 2026
  • python_template Public template

    A template repository for setting up new python packages

    materialyzeai/python_template’s past year of commit activity
    Python 1 0 0 1 Updated Apr 6, 2026
  • matml Public

    Tools for managing the MatML ecosystem

    materialyzeai/matml’s past year of commit activity
    Python 7 BSD-3-Clause 2 0 2 Updated Apr 6, 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 50 2 8 Updated Apr 6, 2026
  • matpes Public

    A foundational potential energy dataset for materials

    materialyzeai/matpes’s past year of commit activity
    Jupyter Notebook 53 BSD-3-Clause 5 0 2 Updated Apr 6, 2026
  • matcalc Public

    A python library for calculating materials properties from the PES

    materialyzeai/matcalc’s past year of commit activity
    Python 136 BSD-3-Clause 35 6 8 Updated Apr 6, 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 453 BSD-3-Clause 96 10 2 Updated Apr 6, 2026
  • flamyngo Public

    Flask frontend for MongoDB

    materialyzeai/flamyngo’s past year of commit activity
    Python 15 BSD-3-Clause 7 0 5 Updated Apr 6, 2026

People

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