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@camsai

CAMSAI

Consortium for the Advancement of Materials Science with AI

CAMSAI

The Consortium for the Advancement of Materials Science with AI (CAMSAI) is an open, vendor-neutral community devoted to advancing materials science with AI. We develop and sustain open-source software, open data standards, and open educational material, and convene the people who build and use them across national laboratories, universities, industry, and independent practice.

CAMSAI participates in the AI Alliance community.

Governance

CAMSAI's charter, decision rules, steering roster, and trademark policy live in one place:

Decisions are made in the open. Routine matters carry by lazy consensus; constitutional changes and project intake require a recorded vote of the Steering Committee, which has seven seats allocated by constituency — a government laboratory, an academic institution, a startup, a cloud infrastructure provider, an HPC hardware provider, an AI models provider, and an independent practitioner — so that no single organization or sector controls the consortium.

Organizations that commit substantial resources, and constituencies that do not hold a seat, have a standing non-voting channel to the Steering Committee through the Advisory Council. Support is acknowledged publicly and confers no governance rights; that separation is deliberate.

Governance documents are versioned by date tag, and each version is published as a consolidated PDF under releases.

CAMSAI is not yet incorporated. It has no separate legal personality and cannot enter contracts, hold property, or receive funds in its own name. See the Charter, section 11, which also says what happens if that is still true at the end of 2027.

Projects

Project What it is
standards Schemas, validation tools, and data models for materials science and AI research
notebooks Interactive notebooks demonstrating CAMSAI tools and workflows
jupyterlite A browser-based environment for running CAMSAI notebooks without local setup
actions Shared continuous-integration workflows for CAMSAI repositories

An AI-native codebase for materials science is incubating for public release later in 2026.

Contributing

Contributions are welcome from anyone. Each repository documents how to build it and what it expects from a change; the terms that apply across all of them are:

  • Code is released under the Apache License 2.0; documents, standards, and educational material under CC BY 4.0.
  • Contributions are accepted under the Developer Certificate of Origin — sign off your commits with git commit -s. There is no contributor license agreement.
  • All CAMSAI spaces are governed by our Code of Conduct.

To propose something that spans the consortium rather than a single project — a new project, a change to how we govern ourselves — open a pull request or an issue against camsai/governance.

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  1. standards standards Public

    CAMSAI Standards provides schemas, validation tools, and data models for materials science and AI research. It ensures data consistency, interoperability, and quality across workflows. Built with P…

    Python 1

  2. jupyterlite jupyterlite Public

    CAMSAI JupyterLite is a lightweight, browser-based environment tailored for AI-driven materials science research. It integrates CAMSAI tools, schemas, and workflows, enabling users to validate data…

    Shell

  3. notebooks notebooks Public

    CAMSAI Notebooks provides interactive Jupyter notebooks for AI-driven materials science research. These notebooks demonstrate the use of CAMSAI tools, schemas, and workflows, offering hands-on exam…

    Jupyter Notebook 1

  4. actions actions Public

    GitHub actions

Repositories

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