Skip to content
@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 452 96

  2. matgl matgl Public

    Graph deep learning library for materials

    Python 527 109

  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 135 34

  6. matpes matpes Public

    A foundational potential energy dataset for materials

    Jupyter Notebook 53 5

Repositories

Showing 10 of 32 repositories
  • matcalc Public

    A python library for calculating materials properties from the PES

    materialyzeai/matcalc’s past year of commit activity
    Python 135 BSD-3-Clause 34 6 7 Updated Mar 30, 2026
  • pymatgen Public Forked from materialsproject/pymatgen

    Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.

    materialyzeai/pymatgen’s past year of commit activity
    Python 0 956 0 6 Updated Mar 30, 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 7 Updated Mar 30, 2026
  • matgl Public

    Graph deep learning library for materials

    materialyzeai/matgl’s past year of commit activity
    Python 527 BSD-3-Clause 109 3 3 Updated Mar 30, 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 Mar 18, 2026
  • flamyngo Public

    Flask frontend for MongoDB

    materialyzeai/flamyngo’s past year of commit activity
    Python 15 BSD-3-Clause 7 0 4 Updated Mar 3, 2026
  • matml Public

    Tools for managing the MatML ecosystem

    materialyzeai/matml’s past year of commit activity
    Python 7 BSD-3-Clause 2 0 1 Updated Mar 2, 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 1 Updated Mar 2, 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 0 Updated Mar 2, 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 452 BSD-3-Clause 95 10 2 Updated Mar 2, 2026

People

This organization has no public members. You must be a member to see who’s a part of this organization.

Most used topics

Loading…