projects.yaml

configuration:
  #
  #
  #
  # ######################################################################
  # Reference: https://github.com/best-of-lists/best-of-generator
  # ######################################################################
  #
  #
  #
  min_stars: 5
  # require_github: True
  max_trending_projects: 5
  allowed_licenses: ["all"]
  require_license: false
  hide_license_risk: true
  require_repo: false
  max_description_length: 300
  min_projectrank: 5
  hide_empty_categories: true
  # allowed_licenses:
  #   [
  #     "Unlicensed",
  #     "Apache-2.0",
  #     "MIT",
  #     "BSD-3-Clause",
  #     "BSD-2-Clause",
  #     "ISC",
  #     "MPL-2.0",
  #     "LGPL-2.1"
  #   ]
  markdown_header_file: "config/header.md"
  markdown_footer_file: "config/footer.md"
  projects_history_folder: "history"

# ################################################################################
# ################################################################################
categories:
  # # categories example.
  # - category: "data-engineering"
  #   title: "Machine Learning & Data Engineering"
  #   subtitle: "Best-of lists about machine learning, data engieering, data science, or other topics related to big data."
  #
  # # categories template.
  # - category: ""
  #   title: ""
  #   subtitle: ""
  - category: "active-learning"
    title: "Active learning"
    subtitle: "Projects that focus on enabling active learning, iterative learning schemes for atomistic ML."
  - category: "community"
    title: "Community resources"
    subtitle: "Projects that collect atomistic ML resources or foster communication within community."
  - category: "datasets"
    title: "Datasets"
    subtitle: "Datasets, databases and trained models for atomistic ML."
  - category: "data-structures"
    title: "Data Structures"
    subtitle: "Projects that focus on providing data structures used in atomistic machine learning."
  - category: "ml-dft"
    title: "Density functional theory (ML-DFT)"
    subtitle: "Projects and models that focus on quantities of DFT, such as density functional approximations (ML-DFA), the charge density, density of states, the Hamiltonian, etc."
  - category: "educational"
    title: "Educational Resources"
    subtitle: "Tutorials, guides, cookbooks, recipes, etc."
  - category: "xai"
    title: "Explainable Artificial intelligence (XAI)"
    subtitle: "Projects that focus on explainability and model interpretability in atomistic ML."
  - category: "ml-esm"
    title: "Electronic structure methods (ML-ESM)"
    subtitle: "Projects and models that focus on quantities of electronic structure methods, which do not fit into either of the categories ML-WFT or ML-DFT."
  - category: "general-tool"
    title: "General Tools"
    subtitle: "General tools for atomistic machine learning."
  - category: "generative"
    title: "Generative Models"
    subtitle: "Projects that implement generative models for atomistic ML."
  - category: "ml-iap"
    title: "Interatomic Potentials (ML-IAP)"
    subtitle: "Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and force fields (ML-FF) for molecular dynamics."
  - category: "language-models"
    title: "Language Models"
    subtitle: "Projects that use (large) language models (LMs, LLMs) or natural language procesing (NLP) techniques for atomistic ML."
  - category: "materials-discovery"
    title: "Materials Discovery"
    subtitle: "Projects that implement materials discovery methods using atomistic ML."
  - category: "math"
    title: "Mathematical tools"
    subtitle: "Projects that implement mathematical objects used in atomistic machine learning."
  - category: "md"
    title: "Molecular Dynamics"
    subtitle: "Projects that simplify the integration of molecular dynamics and atomistic machine learning."
  - category: "probabilistic"
    title: "Probabilistic ML"
    subtitle: "Projects that focus on probabilistic, Bayesian, Gaussian process and adversarial methods for atomistic ML, for optimization, uncertainty quantification (UQ), etc."
  - category: "reinforcement-learning"
    title: "Reinforcement Learning"
    subtitle: "Projects that focus on reinforcement learning for atomistic ML."
  - category: "rep-eng"
    title: "Representation Engineering"
    subtitle: "Projects that offer implementations of representations aka descriptors, fingerprints of atomistic systems, and models built with them, aka feature engineering."
  - category: "rep-learn"
    title: "Representation Learning"
    subtitle: "General models that learn a representations aka embeddings of atomistic systems, such as message-passing neural networks (MPNN)."
  - category: "uip"
    title: "Universal Potentials"
    subtitle: "Machine-learned interatomic potentials (ML-IAP) that have been trained on large, chemically and structural diverse datasets. For materials, this means e.g. datasets that include a majority of the periodic table."
  - category: "unsupervised"
    title: "Unsupervised Learning"
    subtitle: "Projects that focus on unsupervised learning (USL) for atomistic ML, such as dimensionality reduction, clustering and visualization."
  - category: "visualization"
    title: "Visualization"
    subtitle: "Projects that focus on visualization (viz.) for atomistic ML."
  - category: "ml-wft"
    title: "Wavefunction methods (ML-WFT)"
    subtitle: "Projects and models that focus on quantities of wavefunction theory methods, such as Monte Carlo techniques like deep learning variational Monte Carlo (DL-VMC), quantum chemistry methods, etc."

# ################################################################################
# ################################################################################
labels:
  # Label sections: Category duplicate, Programming - specific, Project - specific, ML & Computing - specific, Atomistic Simulation - specific.
  # ########################################
  # Category-mirrored labels
  # ########################################
  - label: "active-learning"
    # category: "active-learning"
    name: "active-learning"
    url: "https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"
  - label: "biomolecules"
    # category: "biomolecules"
    name: "biomolecules"
    # name: "🧬"
    # name: "🧬"
    url: "https://en.wikipedia.org/wiki/Biomolecule"
    description: "Projects that focus on biomolecules, protein structure, protein folding, etc. using atomistic ML."
  - label: "community"
    # category: "community"
    name: "community-resource"
  - label: "datasets"
    # category: "datasets"
    name: "datasets"
  - label: "data-structures"
    # category: "data-structures"
    name: "data-structures"
  - label: "ml-dft"
    # category: "ml-dft"
    name: "ML-DFT"
  - label: "educational"
    # category: "educational"
    name: "educational"
  - label: "xai"
    # category: "xai"
    name: "XAI"
    url: "https://en.wikipedia.org/wiki/Explainable_artificial_intelligence"
  - label: "ml-esm"
    # category: "ml-esm"
    name: "ML-ESM"
  - label: "general-tool"
    # category: "general-tool"
    name: "general-tool"
  - label: "generative"
    # category: "generative"
    name: "generative"
  - label: "language-models"
    # category: "language-models"
    name: "language-models"
    url: "https://en.wikipedia.org/wiki/Language_model"
  - label: "ml-iap"
    # category: "ml-iap"
    name: "ML-IAP"
  - label: "materials-discovery"
    # category: "materials-discovery"
    name: "materials-discovery"
    url: "https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"
  - label: "math"
    # category: "math"
    name: "math"
  - label: "md"
    # category: "md"
    name: "MD"
    url: "https://en.wikipedia.org/wiki/Molecular_dynamics"
  - label: "probabilistic"
    # category: "probabilistic"
    name: "probabilistic"
    description: "Probabilistic methods: Bayesian, Gaussian processes, adversarial attacks, uncertainty quantification (UQ)"
    # # note: UQ has both probabilistic and non-probabilistic approaches. maybe split off into separate label.
  - label: "reinforcement-learning"
    # category: "reinforcement-learning"
    name: "RL"
    url: "https://en.wikipedia.org/wiki/Reinforcement_learning"
  - label: "rep-eng"
    # category: "rep-eng"
    name: "rep-eng"
    url: "https://en.wikipedia.org/wiki/Feature_engineering"
    # url: "https://www.nature.com/articles/s41524-022-00721-x/tables/4"
  - label: "rep-learn"
    # category: "rep-learn"
    name: "rep-learn"
    url: "https://en.wikipedia.org/wiki/Feature_learning"
  - label: "uip"
    # category: "uip"
    name: "UIP"
    url: "https://www.google.com/search?q=universal+interatomic+potential"
  - label: "unsupervised"
    # category: "unsupervised"
    name: "USL"
    url: "https://en.wikipedia.org/wiki/Unsupervised_learning"
  - label: "visualization"
    # category: "visualization"
    name: "viz"
    url: "https://en.wikipedia.org/wiki/Visualization"
  - label: "ml-wft"
    # category: "ml-wft"
    name: "ML-WFT"
  # ########################################
  # Programming - specific labels
  # ########################################
  - label: "lang-c"
    name: "C-lang"
    # image: "https://github.com/JuDFTteam/best-of-atomistic-machine-learning/blob/e143de7faaa0dff3bc6a2c9fbaa46d209e56cb5e/config/images/c.ico"
    description: "Project has a C interface"
  - label: "lang-cpp"
    name: "C++"
    # image: "https://github.com/JuDFTteam/best-of-atomistic-machine-learning/blob/e143de7faaa0dff3bc6a2c9fbaa46d209e56cb5e/config/images/c%2B%2B.ico"
    description: "Project has a C++ interface"
  - label: "lang-fortran"
    name: "Fortran"
    # image: "https://github.com/JuDFTteam/best-of-atomistic-machine-learning/blob/e143de7faaa0dff3bc6a2c9fbaa46d209e56cb5e/config/images/fortran.ico"
    description: "Project has a Fortran interface"
  - label: "lang-java"
    name: "Java"
    # image: "https://github.com/JuDFTteam/best-of-atomistic-machine-learning/blob/073d9ecaf3e28d0f1378e9f660cd4f631bf71fd7/config/images/java.ico"
    description: "Project has a Java interface"
  - label: "lang-js"
    name: "JavaScript"
    # image: "https://github.com/JuDFTteam/best-of-atomistic-machine-learning/blob/073d9ecaf3e28d0f1378e9f660cd4f631bf71fd7/config/images/javascript.ico"
    description: "Project has a JavaScript interface"
  - label: "lang-julia"
    name: "Julia"
    # image: "https://github.com/JuDFTteam/best-of-atomistic-machine-learning/blob/e143de7faaa0dff3bc6a2c9fbaa46d209e56cb5e/config/images/julia.ico"
    description: "Project has a Julia interface"
  - label: "lang-py"
    name: "Python"
    # image: "https://github.com/JuDFTteam/best-of-atomistic-machine-learning/blob/e143de7faaa0dff3bc6a2c9fbaa46d209e56cb5e/config/images/python.ico"
    description: "Project has a Python interface"
  - label: "lang-rust"
    name: "Rust"
    # image: "https://github.com/JuDFTteam/best-of-atomistic-machine-learning/blob/e143de7faaa0dff3bc6a2c9fbaa46d209e56cb5e/config/images/rust.ico"
    description: "Project has a Rust interface"
  # ########################################
  # Project - specific labels
  # ########################################
  - label: "for-fun"
    name: "for-fun"
    description: "For-fun, thought-provoking projects. Not necessarily intended for scientific publication."
  - label: "proprietary"
    name: "proprietary"
    description: "Proprietaryfor software."
    url: "https://en.wikipedia.org/wiki/Proprietary_software"
  - label: "single-paper"
    name: "single-paper"
    description: "Single-paper projects"
  # ########################################
  # ML & Computing - specific labels
  # ########################################
  - label: "ai-agent"
    name: "ai-agent"
    subtitle: "AI agents"
    url: "https://en.wikipedia.org/wiki/Large_language_model#Agency"
    # url: "https://en.wikipedia.org/wiki/Intelligent_agent"
  - label: "automl"
    name: "autoML"
    subtitle: "Automated machine learning (AutoML)"
    url: "https://en.wikipedia.org/wiki/Automated_machine_learning"
  - label: "benchmarking"
    name: "benchmarking"
    description: "Benchmarking ML models"
  - label: "computer-vision"
    name: "computer-vision"
    description: "Computer vision"
    url: "https://en.wikipedia.org/wiki/Computer_vision"
  - label: "experimental-data"
    name: "experimental-data"
    description: "Experimental data"
    url: "https://en.wikipedia.org/wiki/Experimental_physics"
  - label: "FAIR"
    name: "FAIR"
    subtitle: "FAIR Principles"
    url: "https://www.go-fair.org/fair-principles/"
  - label: "general-ml"
    name: "general-ml"
    description: "General machine learning"
  - label: "graph-data"
    name: "graph-data"
    description: "Graph data"
  - label: "generative"
    name: "generative"
    subtitle: "Generative models"
    url: "https://en.wikipedia.org/wiki/Generative_model"
  - label: "htc"
    name: "HTC"
    description: "High-throughput computing"
    url: "https://en.wikipedia.org/wiki/High-throughput_computing"
  - label: "knowledge-base"
    name: "knowledge-base"
    subtitle: "Knowledge base (KB)"
    url: "https://en.wikipedia.org/wiki/Knowledge_base"
  - label: "literature-data"
    name: "literature-data"
    subtitle: "Literature data"
  - label: "model-repository"
    name: "model-repository"
    subtitle: "Model repository"
  - label: "multifidelity"
    name: "multifidelity"
    subtitle: "Multifidelity machine learning"
    url: "https://en.wikipedia.org/wiki/Multifidelity_simulation"
  - label: "multimodal"
    name: "multimodal"
    subtitle: "Multimodal learning"
    url: "https://en.wikipedia.org/wiki/Multimodal_learning"
  - label: "neural-operator"
    name: "neural-operator"
    subtitle: "Neural operators"
    url: "https://en.wikipedia.org/wiki/Neural_operators"
  - label: "pinn"
    name: "pinn"
    subtitle: "Physics-informed neural networks"
    url: "https://en.wikipedia.org/wiki/Physics-informed_neural_networks"
  - label: "pretrained"
    name: "pretrained"
    subtitle: "Pretrained models"
  - label: "qml"
    name: "QML"
    # name: "⚛️"
    # image: ⚛️
    # image: "https://github.com/JuDFTteam/best-of-atomistic-machine-learning/blob/41f7ffd05113cbaacae1ea22b02844c54cd7fca3/config/images/qml.ico"
    description: "Quantum machine learning (QML)"
    url: "https://en.wikipedia.org/wiki/Quantum_machine_learning"
  - label: "recommender"
    name: "recommender"
    description: "Recommender systems"
    url: "https://en.wikipedia.org/wiki/Recommender_system"
  - label: "scikit-learn"
    name: "scikit-learn"
    description: "Heavily integrates with or extends scikit-learn"
  - label: "small-data"
    name: "small-data"
    description: "Project optimizes for small data, limited data availability"
  - label: "transfer-learning"
    name: "transfer-learning"
    description: "Project implements a transfer learning technique"
    url: "https://en.wikipedia.org/wiki/Transfer_learning"
  - label: "transformer"
    name: "transformer"
    description: "Project uses transformer deep learning architecture"
    url: "https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"
  - label: "workflows"
    name: "workflows"
    description: "Workflows, pipelines, MLOps"
  # ########################################
  # Atomistic Simulation - specific labels
  # ########################################
  - label: "catalysis"
    name: "catalysis"
    description: "Catalysis"
    url: "https://en.wikipedia.org/wiki/Catalysis"
  - label: "cheminformatics"
    name: "cheminformatics"
    description: "Cheminformatics"
    url: "https://en.wikipedia.org/wiki/Cheminformatics"
  - label: "density-of-states"
    name: "density-of-states"
    description: "Density of states"
    url: "https://en.wikipedia.org/wiki/Density_of_states"
  - label: "drug-discovery"
    name: "drug-discovery"
    # name: "💊"
    # name: "💊"
    description: "Drug discovery"
    url: "https://en.wikipedia.org/wiki/Drug_design#Computer-aided_drug_design"
  - label: "electrostatics"
    name: "electrostatics"
    # name: "⚡"
    # image: "⚡"
    description: "Electrostatics, partial charges, charge transfer, charge equilibration"
    url: "https://en.wikipedia.org/wiki/Electrostatics"
  - label: "sampling"
    name: "sampling"
    description: "Projects that simplify the application of machine-learning related atomistic sampling methods."
    # reference: issue #175 https://github.com/JuDFTteam/best-of-atomistic-machine-learning/issues/175
  - label: "excited-states"
    name: "excited-states"
    description: "Excited states"
    url: "https://en.wikipedia.org/wiki/Excited_state"
  - label: "long-range"
    name: "long-range"
    description: "Long-range interactions"
    # url: "https://en.wikipedia.org/wiki/Excited_state"
  - label: "magnetism"
    name: "magnetism"
    # name: "🧲"
    # name: "🧲"
    description: "Magnetism"
  - label: "material-defect"
    name: "material-defect"
    description: "Material defects"
    url: "https://en.wikipedia.org/wiki/Crystallographic_defect"
  - label: "optical-properties"
    name: "optical-properties"
    # name: "👁️"
    # name: "🌈"
    # name: "🌈"
    description: "Optical properties"
    url: "https://en.wikipedia.org/wiki/Optical_properties"
  - label: "spectroscopy"
    name: "spectroscopy"
    description: "Spectroscopy"
    url: "https://en.wikipedia.org/wiki/Spectroscopy"
  - label: "structure-optimization"
    name: "structure-optimization"
    description: "Materials structure optimization"
    url: "https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"
  - label: "structure-prediction"
    name: "structure-prediction"
    description: "Materials structure prediction"
  - label: "superconductors"
    name: "superconductors"
    # name: "🧘"
    # name: "🧘🏾"
    description: "Superconductors"
    url: "https://en.wikipedia.org/wiki/Superconductivity"
  - label: "surface-science"
    name: "surface-science"
    # name: "🪞"
    # name: "🪞"
    description: "Surface science"
    url: "https://en.wikipedia.org/wiki/Surface_science"
  - label: "transport-phenomena"
    name: "transport-phenomena"
    # name: "🔥"
    # name: "🔥"
    description: "Transport phenomena"
    url: "https://en.wikipedia.org/wiki/Transport_phenomena"
  - label: "xrd"
    name: "XRD"
    # name: "🩻"
    # name: "🩻"
    description: "X-ray diffraction"
    url: "https://en.wikipedia.org/wiki/X-ray_crystallography"

# ################################################################################
# ################################################################################
projects:
  # ################################################################################
  # DOCUMENTATION
  # ################################################################################
  #
  # # project generic example (not matching this list's categories etc.)
  # - name: best-of-ml-python
  #   category: data-engineering
  #   labels: ["lang-py"]
  #   github_id: ml-tooling/best-of-ml-python
  #
  # # project full template.
  # # reference https://github.com/best-of-lists/best-of-generator#project-properties
  # - name:
  #   resource:
  #   category:
  #   description:
  #   labels:
  #   homepage:
  #   docs_url:
  #   group:
  #   group_id:
  #   license:
  #   show:
  #   ignore:
  #   github_id:
  #   gitlab_id:
  #   pypi_id:
  #   conda_id:
  #   npm_id:
  #   dockerhub_id:
  #   maven_id:
  #
  # Style guide / guidelines for list best-of-atomistic-machine-learning (this list).
  #
  # - Use the simple template for projects and resources.
  # - Putting property values in quotes is mostly not needed, see examples below.
  # - Resources are those that don't have a repo (github_id etc.). See examples below.
  # - URLs: The project link in the README.md will point to link in prop, in order:
  #   homepage, docs_url, one of {github_id, gitlab_id, etc., whichever provided}.
  #   This means that, in fact, the README entry does not use the other links at all.
  #   For that reason, in most cases only provide the repo_id URL. It usually points to
  #   the other URLs, while the latter don't always point to the repo. If the other
  #   URLs exist, but the repo doesn't point to them, add them as props, but comment
  #   them out. See examples below.
  # - Special case resource-like projects (e.g. datasets, educational, community resource).
  #   Often, for these projects, the 'homepage' prop is the main URL. However, quality metrics
  #   are only collected from repo URLs and package managers, not individual homepages. For that
  #   reason, such projects then usually get the 'resource: true' property (the default is
  #   'resource: false'). This setting disables metrics collection for that project. Such projects
  #   get listed in the READMe as a static, alphabetically ordered list appended to their category,
  #   followed by the non-resource, metrics-ordered projects. Preference should be given to metrics
  #   ordering whenever possible. Therefore, if a resource-like project is added and can be
  #   associated with a directly related repo (e.g. a client for a database), then add both the
  #   'homepage' and repo URL (github_id or gitlab_id) and DON't set the resource property.
  #   See examples categories community, datasets, educationl.
  # - If a project is a subfolder of a repo, provide both props homepage (link to the repo
  #   subfolder containing the project) and the repo id (e.g., github_id). See example
  #   project DM21 below. That way, the project quality score is calculuated, and the
  #   project link points to the correct location. Don't set resource: true.
  # - Category & labels: Every category has a corresponding label, not every label has a
  #   corresponding category. View the category as the emphasized label. Labels / tags are
  #   generally preferred over tree-based classification (in this case, categories, one-
  #   dimensional). So, be generous with labeling. The label selected as category should
  # - If the repo_id's project description is missing or unhelpful, add description prop.
  #   Prefer to copy headline oneliner from project's README, if useful. If the project is
  #   tied to a preprint or paper and the repo README doesn't point to it, append the DOI
  #   link to the description.
  # - You can use groups props (group_id = group definition, group = group association)
  #   for keeping track of related projects (eg all from same devlopers; though that
  #   is usually clear from the repo_id parent folder.) However, comment them out, because
  #   they might interfer when grouped projects are in different categories (I think).
  # - If a project's category is changed, view this as emphasized label change.
  #   Consequently, add the old category as label, if not incorrect.
  #
  # # project simple template.
  # - name:
  #   github_id:
  #   category:
  #   labels: []
  #   license:
  #
  # # resource simple template.
  # - name:
  #   resource: true
  #   category:
  #   homepage:
  #
  # ################################################################################

  # ################################################################################
  # COMMUNITY with resource: true (list here alphabetically)
  # ################################################################################
- name: AI for Science Map
  resource: true
  category: community
  license: GPL-3.0 license
  homepage: https://www.air4.science/map
  description: Interactive mindmap of the AI4Science research field, including atomistic machine learning, including papers, packages, learning resources.
- name: Atomic Cluster Expansion
  resource: true
  category: community
  homepage: https://cortner.github.io/ACEweb/
  # group: ACE
  description: Atomic Cluster Expansion (ACE) community homepage.
- name: CrystaLLM
  resource: true
  homepage: https://crystallm.com
  category: community
  labels: ["language-models", "generative", "pretrained", "transformer"]
  license: https://materialis.ai/terms.html
  # package managers: none
  description: Generate a crystal structure from a composition.
- name: GAP-ML.org community homepage
  resource: true
  homepage: https://gap-ml.org/
  category: community
  labels: ["ml-iap"]
  # license: # none found
  # package managers: none
- name: matsci.org
  resource: true
  category: community
  homepage: https://matsci.org/
  description: "A community forum for the discussion of anything materials science, with a focus on computational materials science research."
- name: Matter Modeling Stack Exchange - Machine Learning
  resource: true
  category: community
  homepage: https://mattermodeling.stackexchange.com/questions/tagged/machine-learning
  description: Forum StackExchange, site Matter Modeling, ML-tagged questions.
  # ################################################################################
  # COMMUNITY with resource: false (sorted by CI/CD)
  # ################################################################################
- name: The Collection of Database and Dataset Resources in Materials Science
  category: community
  labels: ["datasets"]
  # license: # none found
  github_id: sedaoturak/data-resources-for-materials-science
- name: Graph-based Deep Learning Literature
  category: community
  labels: ["general-ml", "rep-learn"]
  license: MIT
  github_id: naganandy/graph-based-deep-learning-literature
- name: Best-of Machine Learning with Python
  category: community
  labels: ["general-ml", "lang-py"]
  license: "CC-BY-4.0"
  github_id: ml-tooling/best-of-ml-python
- name: Awesome Materials Informatics
  category: community
  license: https://github.com/tilde-lab/awesome-materials-informatics#license
  github_id: tilde-lab/awesome-materials-informatics
- name: GitHub topic materials-informatics
  description: GitHub topic materials-informatics
  category: community
  homepage: https://github.com/topics/materials-informatics
- name: A Highly Opinionated List of Open-Source Materials Informatics Resources
  category: community
  license: MIT
  github_id: ncfrey/resources
- name: MatBench
  category: community
  labels: ["datasets", "benchmarking", "model-repository"]
  license: MIT
  github_id: materialsproject/matbench
  pypi_id: matbench
- name: MatBench Discovery
  category: community
  labels: ["datasets", "benchmarking", "model-repository"]
  license: MIT
  github_id: janosh/matbench-discovery
  pypi_id: matbench-discovery
- name: AI for Science Resources
  category: community
  license: GPL-3.0 license
  homepage: https://github.com/divelab/AIRS/blob/main/Overview/resources.md
  github_id: divelab/AIRS
  description: List of resources for AI4Science research, including learning resources.
- name: MateriApps
  category: community
  homepage: https://ma.issp.u-tokyo.ac.jp/en/
  # license: # none found
  description: "A Portal Site of Materials Science Simulation."
- name: Awesome Neural Geometry
  github_id: neurreps/awesome-neural-geometry
  category: community
  labels: ["educational", "rep-learn"]
  # license: # none found
  # package managers: none
- name: MoLFormers UI
  homepage: https://molformer.res.ibm.com/
  github_id: IBM/molformer
  category: community
  labels: ["transformer", "language-models", "pretrained", "drug-discovery"]
  license: Apache-2.0
  # package managers: none
  description: "A family of foundation models trained on chemicals."
- name: GT4SD - Generative Toolkit for Scientific Discovery
  homepage: https://huggingface.co/GT4SD
  github_id: GT4SD/gt4sd-core
  category: community
  labels: ["generative", "pretrained", "drug-discovery", "model-repository"]
  license: MIT
  # package managers: none
  description: "Gradio apps of generative models in GT4SD."
- name: GNoME Explorer
  homepage: https://next-gen.materialsproject.org/materials/gnome
  github_id: google-deepmind/materials_discovery
  category: community
  labels: ["datasets", "materials-discovery"]
  license: Apache-2.0
  description: "Graph Networks for Materials Exploration Database"
- name: Does this material exist?
  homepage: https://thismaterialdoesnotexist.com/
  github_id: ml-evs/this-material-does-not-exist
  category: community
  labels: ["for-fun", "materials-discovery"]
  license: MIT
- name: optimade.science
  homepage: https://optimade.science
  github_id: tilde-lab/optimade.science
  category: community
  labels: ["datasets"]
  license: MIT
- name: OpenML
  github_id: openml/OpenML
  # homepage: https://www.openml.org
  # resource: true
  category: community
  labels: ["datasets"]
  license: BSD-3
  # package managers: none
- name: MADICES Awesome Interoperability
  github_id: MADICES/MADICES.github.io
  homepage: MADICES/MADICES.github.io/blob/main/docs/awesome_interoperability.md
  description: Linked data interoperability resources of the Machine-actionable data interoperability for the chemical sciences (MADICES) project
  # homepage: https://madices.github.io/docs/awesome_interoperability
  category: community
  labels: ["datasets"]
  license: MIT
  # package managers: none
- name: Awesome Neural SBI
  github_id: smsharma/awesome-neural-sbi
  category: community
  labels: ["active-learning"]
  license: MIT
  # package managers: none
- name: Geometric-GNNs
  github_id: AlexDuvalinho/geometric-gnns
  category: community
  labels: ["datasets", "educational", "rep-learn"]
  # license: # none found
  # package managers: none
- name: Awesome-Graph-Generation
  description: A curated list of up-to-date graph generation papers and resources.
  github_id: yuanqidu/awesome-graph-generation
  category: community
  labels: ["rep-learn"]
  # license: # none found
  # package managers: none
- name: Neural-Network-Models-for-Chemistry
  github_id: Eipgen/Neural-Network-Models-for-Chemistry
  category: community
  labels: ["rep-learn"]
  # license: # none found
  # package managers: none
- name: AI for Science paper collection
  github_id: sherrylixuecheng/AI_for_Science_paper_collection
  category: community
  license: Apache-2.0
  # package managers: none
  # ################################################################################
  # DATASETS with resource: true (list here alphabetically)
  # ################################################################################
- name: Alexandria Materials Database
  resource: true
  description: A database of millions of theoretical crystal structures (3D, 2D and 1D) discovered by machine learning accelerated density functional theory high-throughput searches.
  homepage: https://alexandria.icams.rub.de/
  category: datasets
  license: CC-BY-4.0
  # package managers: none
- name: "Catalysis Hub"
  resource: true
  category: datasets
  description: "A web-platform for sharing data and software for computational catalysis research!"
  homepage: https://www.catalysis-hub.org/
- name: Citrination Datasets
  resource: true
  category: datasets
  license: MIT
  homepage: https://citrination.com/
  description: AI-Powered Materials Data Platform. Open Citrination has been decommissioned.
- name: crystals.ai
  resource: true
  category: datasets
  description: Curated datasets for reproducible AI in materials science.
  homepage: https://crystals.ai/
- name: "DeepChem Models"
  resource: true
  category: datasets
  labels: ["model-repository", "pretrained", "language-models"]
  homepage: https://huggingface.co/DeepChem
  description: DeepChem models on HuggingFace.
- name: Graphs of Materials Project 20190401
  resource: true
  description: The dataset used to train the MEGNet interatomic potential.
  homepage: https://figshare.com/articles/dataset/Graphs_of_Materials_Project_20190401/8097992
  category: datasets
  labels: ["ml-iap"]
  license: MIT
  # package managers: none
- name: HME21 Dataset
  resource: true
  description: High-temperature multi-element 2021 dataset for the PreFerred Potential (PFP) https://doi.org/10.1038/s41467-022-30687-9.
  homepage: https://doi.org/10.6084/m9.figshare.19658538
  category: datasets
  labels: ["uip"]
  # license: # none found
  # package managers: none
- name: JARVIS-Leaderboard
  resource: true
  category: datasets
  labels: ["model-repository", "benchmarking", "community", "educational"]
  homepage: https://pages.nist.gov/jarvis_leaderboard/
  license: https://github.com/usnistgov/jarvis_leaderboard/blob/main/LICENSE.rst
  github_id: usnistgov/jarvis_leaderboard
- name: Materials Project - Charge Densities
  resource: true
  category: datasets
  # license: # none found
  homepage: https://materialsproject.org/ml/charge_densities
  description: Materials Project has started offering charge density information available for download via their public API.
- name: Materials Project Trajectory (MPtrj) Dataset
  description: The dataset used to train the CHGNet universal potential.
  resource: true
  homepage: https://figshare.com/articles/dataset/Materials_Project_Trjectory_MPtrj_Dataset/23713842
  category: datasets
  labels: ["uip"]
  license: MIT
  # package managers: none
- name: matterverse.ai
  resource: true
  category: datasets
  homepage: https://matterverse.ai/
  description: Database of yet-to-be-sythesized materials predicted using state-of-the-art machine learning algorithms
- name: MPF.2021.2.8
  resource: true
  description: The dataset used to train the M3GNet universal potential.
  homepage: https://figshare.com/articles/dataset/MPF_2021_2_8/19470599
  category: datasets
  labels: ["uip"]
  # license: # none found
  # package managers: none
- name: NRELMatDB
  resource: true
  category: datasets
  homepage: https://materials.nrel.gov/
  description: "Computational materials database with the specific focus on materials for renewable energy applications including, but not limited to, photovoltaic materials, materials for photo-electrochemical water splitting, thermoelectrics, etc."
  # license: # none found
  # package managers: none
- name: "Quantum-Machine.org Datasets"
  resource: true
  category: datasets
  homepage: http://quantum-machine.org/datasets/
  description: "Collection of datasets, including QM7, QM9, etc. MD, DFT. Small organic molecules, mostly."
- name: sGDML Datasets
  resource: true
  category: datasets
  homepage: http://sgdml.org/#datasets
  description: MD17, MD22, DFT datasets.
- name: MoleculeNet
  resource: true
  category: datasets
  labels: ["benchmarking"]
  # license: # none found
  homepage: https://moleculenet.org/
  description: "A Benchmark for Molecular Machine Learning"
- name: ZINC15
  resource: true
  category: datasets
  labels: ["graph", "biomolecules"]
  # license: # none found
  homepage: https://zinc15.docking.org/
  description: "A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable compounds in ready-to-dock, 3D formats."
- name: ZINC20
  resource: true
  category: datasets
  labels: ["graph", "biomolecules"]
  # license: # none found
  homepage: https://zinc.docking.org/
  description: "A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable compounds in ready-to-dock, 3D formats."
  # ################################################################################
  # DATASETS with resource: false (sorted by CI/CD)
  # ################################################################################
- name: Open Databases Integration for Materials Design (OPTIMADE)
  category: datasets
  license: CC-BY-4.0
  github_id: Materials-Consortia/OPTIMADE
  # homepage: https://optimade.org
  # description: Specification of a common REST API for access to 25+ materials databases
- name: Visual Graph Datasets
  # resource: false
  category: datasets
  labels: ["xai", "rep-learn"]
  license: "MIT"
  github_id: aimat-lab/visual_graph_datasets
- name: 3DSC Database
  # resource: false
  category: datasets
  labels: ["superconductors", "materials-discovery"]
  license: https://github.com/aimat-lab/3DSC/blob/main/LICENSE.md
  github_id: aimat-lab/3DSC
- name: ANI-1 Dataset
  # resource: true
  category: datasets
  github_id: isayev/ANI1_dataset
- name: ANI-1x Datasets
  # resource: true
  category: datasets
  github_id: aiqm/ANI1x_datasets
- name: COMP6 Benchmark dataset
  # resource: true
  category: datasets
  github_id: isayev/COMP6
- name: MoleculeNet Leaderboard
  # resource: true
  category: datasets
  description:
  labels: ["benchmarking"]
  github_id: deepchem/moleculenet
- name: GEOM
  # resource: true
  category: datasets
  labels: ["drug-discovery"]
  github_id: learningmatter-mit/geom
- name: OpenKIM
  # resource: true
  category: datasets
  labels: ["model-repository", "knowledge-base", "pretrained"]
  homepage: https://openkim.org/
  # docs_url: https://kim-api.readthedocs.io/
  github_id: openkim/kim-api
- name: linear-regression-benchmarks
  # resource: true
  category: datasets
  labels: ["benchmarking", "single-paper"]
  github_id: BingqingCheng/linear-regression-benchmarks
- name: FAIR Chemistry datasets
  category: datasets
  labels: ["catalysis"]
  license: MIT
  description: Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project.
  github_id: FAIR-Chem/fairchem
  pypi_id: fairchem-core
- name: Meta Open Materials 2024 (OMat24) Dataset
  description: Contains over 100 million Density Functional Theory calculations focused on structural and compositional diversity.
  homepage: https://huggingface.co/datasets/fairchem/OMAT24
  github_id: https://github.com/FAIR-Chem/fairchem
  category: datasets
  # labels: ["ml-iap", "uip", "catalysis"]
  license: CC-BY-4.0
  pypi_id: fairchem-core
- name: fairchem
  category: ml-iap
  labels: ["pretrained", "uip", "rep-learn", "catalysis"]
  description: FAIR Chemistry's library of machine learning methods for chemistry. Formerly known as Open Catalyst Project.
  # homepage: https://opencatalystproject.org/
  github_id: FAIR-Chem/fairchem
  pypi_id: fairchem-core
- name: MPContribs
  category: datasets
  license: MIT
  github_id: materialsproject/MPContribs
  pypi_id: mpcontribs-client
- name: SciGlass
  category: datasets
  license: MIT
  github_id: drcassar/SciGlass
  # alt. github_id: CitrineInformatics-ERD-public/SciGlass
- name: SPICE
  category: datasets
  labels: ["ml-iap", "md"]
  license: MIT
  github_id: openmm/spice-dataset
  # package managers: none
- name: 2DMD dataset
  category: datasets
  labels: ["material-defect"]
  # license: # none found
  homepage: https://github.com/HSE-LAMBDA/ai4material_design/blob/main/docs/DATA.md
  github_id: HSE-LAMBDA/ai4material_design
- name: ATOM3D
  github_id: drorlab/atom3d
  category: datasets
  labels: ["biomolecules", "benchmarking"]
  license: MIT
  pypi_id: atom3d
- name: "QH9"
  description: "A Quantum Hamiltonian Prediction Benchmark"
  homepage: https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench/QH9
  github_id: divelab/AIRS
  category: datasets
  labels: ["ml-dft"]
  license: CC-BY-NC-SA-4.0
  # package managers: none
- name: paper-data-redundancy
  github_id: mathsphy/paper-data-redundancy
  category: datasets
  labels: ["small-data", "single-paper"]
  license: BSD-3-Clause
  # package managers: none
- name: Materials Data Facility (MDF)
  category: datasets
  homepage: https://www.materialsdatafacility.org
  github_id: materials-data-facility/connect_client
  description: "A simple way to publish, discover, and access materials datasets. Publication of very large datasets supported (e.g., multi-TB or millions of files)"
- name: OPTIMADE Python tools
  category: datasets
  github_id: Materials-Consortia/optimade-python-tools
  license: MIT
  pypi_id: optimade
  conda_id: conda-forge/optimade
- name: OPTIMADE providers dashboard
  category: datasets
  homepage: https://www.optimade.org/providers-dashboard/
  github_id: Materials-Consortia/providers-dashboard
  # license: # none found
- name: nep-data
  category: datasets
  gitlab_id: brucefan1983/nep-data
  labels: ["ml-iap", "md", "transport-phenomena"]
  # license: # none found
  description: "Data related to the NEP machine-learned potential of GPUMD."
- name: tmQM_wB97MV Dataset
  github_id: ulissigroup/tmqm_wB97MV
  category: datasets
  labels: ["catalysis", "rep-learn"]
  # license: # none found
  # package managers: none
- name: GDB-9-Ex9 and ORNL_AISD-Ex
  github_id: ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python
  category: datasets
  # license: # none found
  # package managers: none
- name: The Perovskite Database Project
  # homepage: https://www.perovskitedatabase.com
  description: Perovskite Database Project aims at making all perovskite device data, both past and future, available in a form adherent to the FAIR data principles
  github_id: Jesperkemist/perovskitedatabase
  category: datasets
  labels: ["community"]
  # license: # none found
  # package managers: none
- name: load-atoms
  github_id: jla-gardner/load-atoms
  category: datasets
  labels: ["data-structures"]
  license: MIT
  pypi_id: load-atoms
- name: AIS Square
  description: "A collaborative and open-source platform for sharing AI for Science datasets, models, and workflows. Home of the OpenLAM initiative."
  # homepage: https://www.aissquare.com/
  github_id: deepmodeling/AIS-Square
  category: datasets
  labels: ["community", "model-repository"]
  license: LGPL-3.0
  # package managers: none
  # ################################################################################
  # EDUCATIONAL with resource: true (list here alphabetically)
  # ################################################################################
- name: AI for Science 101
  homepage: https://ai4science101.github.io/
  resource: true
  category: educational
  labels: ["community", "rep-learn"]
  # license: # none found
  # package managers: none
- name: AL4MS 2023 workshop tutorials
  resource: true
  homepage: https://sites.utu.fi/al4ms2023/media-and-tutorials/
  category: educational
  labels: ["active-learning"]
  # license: # none found
  # package managers: none
- name: "Quantum Chemistry in the Age of Machine Learning"
  resource: true
  category: educational
  homepage: https://www.elsevier.com/books-and-journals/book-companion/9780323900492
  description: Book, 2022.
  # ################################################################################
  # EDUCATIONAL with resource: false (sorted by CI/CD)
  # ################################################################################
- name: COSMO Software Cookbook
  category: educational
  # docs_url: https://software-cookbook.rtfd.io/
  github_id: lab-cosmo/software-cookbook
- name: iam-notebooks
  category: educational
  github_id: ceriottm/iam-notebooks
- name: ML-in-chemistry-101
  category: educational
  github_id: BingqingCheng/ML-in-chemistry-101
- name: Deep Learning for Molecules and Materials Book
  category: educational
  license: https://github.com/whitead/dmol-book/blob/main/LICENSE
  homepage: https://dmol.pub/
  github_id: whitead/dmol-book
- name: jarvis-tools-notebooks
  category: educational
  # description: A Google-Colab Notebook Collection for Materials Design
  license: "NIST"
  github_id: JARVIS-Materials-Design/jarvis-tools-notebooks
- name: BestPractices
  category: educational
  github_id: anthony-wang/BestPractices
- name: chemrev-gpr
  category: educational
  github_id: gabor1/chemrev-gpr
- name: MAChINE
  category: educational
  github_id: aimat-lab/MAChINE
- name: MALA Tutorial
  category: educational
  github_id: mala-project/mala_tutorial
- name: PiNN Lab
  category: educational
  github_id: Teoroo-CMC/PiNN_lab
  description: Material for running a lab session on atomic neural networks.
- name: "Data Handling, DoE and Statistical Analysis for Material Chemists"
  category: educational
  github_id: Teoroo-CMC/DoE_Course_Material
- name: DeepLearningLifeSciences
  category: educational
  github_id: deepchem/DeepLearningLifeSciences
- name: "Machine Learning for Materials Hard and Soft"
  category: educational
  # homepage: https://www.esi.ac.at/events/e457/
  github_id: CompPhysVienna/MLSummerSchoolVienna2022
- name: AI4Science101
  category: educational
  # homepage: https://ai4science101.deepmodeling.com/
  github_id: deepmodeling/AI4Science101
- name: "RDKit Tutorials"
  category: educational
  license: https://github.com/rdkit/rdkit-tutorials/blob/master/LICENSE
  github_id: rdkit/rdkit-tutorials
- name: "Applied AI for Materials"
  category: educational
  github_id: WardLT/applied-ai-for-materials
- name: MLDensity_tutorial
  category: educational
  github_id: bfocassio/MLDensity_tutorial
- name: LAMMPS-style pair potentials with GAP
  github_id: victorprincipe/pair_potentials
  category: educational
  labels: ["ml-iap", "md", "rep-eng"]
  # license: # none found
  # package managers: none
- name: DSECOP
  github_id: GDS-Education-Community-of-Practice/DSECOP
  category: educational
  license: CCO-1.0
  # package managers: none
- name: Geometric GNN Dojo
  # note: chaitjo/geometric-gnn-dojo duplicate on purpose. this one for educational.
  homepage: https://github.com/chaitjo/geometric-gnn-dojo/blob/main/geometric_gnn_101.ipynb
  github_id: chaitjo/geometric-gnn-dojo
  category: educational
  labels: ["rep-learn"]
  license: MIT
  # package managers: none
  description: "New to geometric GNNs: try our practical notebook, prepared for MPhil students at the University of Cambridge."
- name: MACE-tutorials
  description: Another set of tutorials for the MACE interatomic potential by one of the authors.
  github_id: ilyes319/mace-tutorials
  category: educational
  labels: ["ml-iap", "rep-learn", "md"]
  license: MIT
  # package managers: none
- name: OPTIMADE Tutorial Exercises
  github_id: Materials-Consortia/optimade-tutorial-exercises
  category: educational
  labels: ["datasets"]
  license: MIT
- name: ML for catalysis tutorials
  github_id: ulissigroup/ml_catalysis_tutorials
  # homepage: https://ulissigroup.cheme.cmu.edu/ml_catalysis_tutorials
  category: educational
  license: MIT
  # package managers: none
  description: A jupyter book repo for tutorial on how to use OCP ML models for catalysis.
- name: AI4Chemistry course
  github_id: schwallergroup/ai4chem_course
  description: EPFL AI for chemistry course, Spring 2023. https://schwallergroup.github.io/ai4chem_course
  category: educational
  labels: ["chemistry"]
  license: MIT
  # package managers: none
- name: AI4ChemMat Hands-On Series
  github_id: ai4chemmat/ai4chemmat.github.io
  # homepage: https://ai4chemmat.github.io/
  category: educational
  license: MPL-2.0
  description: Hands-On Series organized by Chemistry and Materials working group at Argonne Nat Lab.
  # package managers: none
  # ################################################################################
  # ALL THE REST (sorted by CI/CD)
  # ################################################################################
- name: PACE
  category: md
  description: "The LAMMPS ML-IAP `pair_style pace`, aka Atomic Cluster Expansion (ACE), aka ML-PACE, https://cortner.github.io/ACEweb/software/."
  # homepage: https://cortner.github.io/ACEweb/software/
  # docs_url: https://docs.lammps.org/pair_pace.html
  # group_id: ACE
  license: https://github.com/ICAMS/lammps-user-pace/blob/main/LICENSE
  github_id: ICAMS/lammps-user-pace
- name: Pacemaker
  category: ml-iap
  description: Python package for fitting atomic cluster expansion (ACE) potentials
  homepage: https://cortner.github.io/ACEweb/software/
  # docs_url: https://pacemaker.readthedocs.io
  # group_id: ACE
  license: https://github.com/ICAMS/python-ace/blob/master/LICENSE.md
  github_id: ICAMS/python-ace
  pypi_id: python-ace
- name: TensorPotential
  category: ml-iap
  description: "Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic structure calculations data and perform computations using these potentials [...] Only Atomic Cluster Expansion (ACE) is available at the moment."
  homepage: https://cortner.github.io/ACEweb/software/
  # group_id: ACE
  license: https://github.com/ICAMS/TensorPotential/blob/main/LICENSE.md
  github_id: ICAMS/TensorPotential
- name: ACE1.jl
  category: ml-iap
  labels: ["lang-julia"]
  homepage: https://acesuit.github.io/
  # docs_url: https://acesuit.github.io/ACE1pack.jl
  license: https://github.com/ACEsuit/ACE1.jl/blob/main/ASL.md
  # group_id: ACE
  github_id: ACEsuit/ACE1.jl
- name: ACE1Pack.jl
  category: ml-iap
  description: Provides convenience functionality for the usage of ACE1.jl, ACEfit.jl, JuLIP.jl for fitting interatomic potentials using the ACE model.
  labels: ["lang-julia"]
  homepage: https://github.com/ACEsuit/ACE1pack.jl
  docs_url: https://acesuit.github.io/ACE1pack.jl
  # group_id: ACE
  github_id: ACEsuit/ACE1pack.jl
- name: MACE
  category: ml-iap
  # homepage: https://acesuit.github.io/
  # group_id: ACE
  license: "MIT"
  github_id: ACEsuit/mace
- name: MACE-Jax
  category: ml-iap
  # homepage: https://acesuit.github.io/
  # group_id: ACE
  license: "MIT"
  github_id: ACEsuit/mace-jax
- name: MACE-Layer
  category: rep-learn
  # homepage: https://acesuit.github.io/
  # group_id: ACE
  github_id: ACEsuit/mace-layer
- name: ACE.jl
  category: ml-iap
  labels: ["lang-julia"]
  # homepage: https://acesuit.github.io/
  # group_id: ACE
  license: https://github.com/ACEsuit/ACE.jl/blob/main/license/mit.md
  github_id: "ACEsuit/ACE.jl"
- name: ACE Workflows
  category: ml-iap
  labels: ["lang-julia", "workflows"]
  # homepage: https://acesuit.github.io/
  # group_id: ACE
  github_id: ACEsuit/ACEworkflows
- name: ACEhamiltonians
  category: ml-dft
  description: Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-state systems.
  labels: ["lang-julia"]
  license: "MIT"
  # homepage: https://acesuit.github.io/
  # group_id: ACE
  github_id: ACEsuit/ACEhamiltonians.jl
- name: Polynomials4ML.jl
  category: math
  labels: ["lang-julia"]
  # homepage: https://acesuit.github.io/
  # group_id: ACE
  github_id: ACEsuit/Polynomials4ML.jl
- name: ACEatoms
  category: general-tool
  labels: ["lang-julia"]
  # homepage: https://acesuit.github.io/
  # group_id: ACE
  license: https://github.com/ACEsuit/ACEatoms.jl/blob/main/ASL.md
  github_id: ACEsuit/ACEatoms.jl
- name: ACEfit
  category: ml-iap
  description:
  labels: ["lang-julia"]
  # homepage: https://acesuit.github.io/
  # group_id: ACE
  github_id: ACEsuit/ACEfit.jl
- name: ACEHAL
  category: active-learning
  description: Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials
  labels: ["lang-julia"]
  # labels: ["lang-julia", "active-learning"]
  # homepage: https://acesuit.github.io/
  # group_id: ACE
  github_id: ACEsuit/ACEHAL
- name: DeePKS-kit
  category: ml-dft
  github_id: deepmodeling/deepks-kit
- name: cmlkit
  category: rep-eng
  labels: ["benchmarking"]
  # homepage: https://marcel.science/repbench/
  github_id: sirmarcel/cmlkit
  pypi_id: cmlkit
- name: glp
  category: ml-iap
  github_id: sirmarcel/glp
- name: "gkx: Green-Kubo Method in JAX"
  category: rep-learn
  labels: ["transport-phenomena"]
  github_id: sirmarcel/gkx
- name: Scikit-Matter
  category: general-tool
  labels: ["scikit-learn"]
  # docs_url: https://scikit-matter.readthedocs.io
  github_id: scikit-learn-contrib/scikit-matter
  pypi_id: skmatter
  conda_id: conda-forge/skmatter
- name: Equisolve
  category: general-tool
  labels: ["ml-iap"]
  # docs_url: https://lab-cosmo.github.io/equisolve
  github_id: lab-cosmo/equisolve
- name: Metatensor
  category: data-structures
  labels: ["lang-rust", "lang-c", "lang-cpp", "lang-py"]
  # docs_url: https://lab-cosmo.github.io/metatensor
  github_id: lab-cosmo/metatensor
- name: Sketchmap
  labels: ["lang-cpp"]
  category: unsupervised
  github_id: lab-cosmo/sketchmap
- name: Wigner Kernels
  category: math
  description: Collection of programs to benchmark Wigner kernels.
  labels: ["benchmarking"]
  github_id: lab-cosmo/wigner_kernels
- name: Rascaline
  category: rep-eng
  labels: ["lang-rust", "lang-cpp"]
  # docs_url: https://luthaf.fr/rascaline/
  github_id: Luthaf/rascaline
- name: Librascal
  category: rep-eng
  # docs_url: https://lab-cosmo.github.io/librascal/
  github_id: lab-cosmo/librascal
- name: NICE
  category: rep-eng
  description: NICE (N-body Iteratively Contracted Equivariants) is a set of tools designed for the calculation of invariant and covariant atomic structure representations.
  # docs_url: https://lab-cosmo.github.io/nice/
  github_id: lab-cosmo/nice
- name: SpheriCart
  category: math
  # docs_url: https://sphericart.readthedocs.io/
  github_id: lab-cosmo/sphericart
  pypi_id: sphericart
- name: Chemiscope
  category: visualization
  labels: ["lang-js"]
  # homepage: https://chemiscope.org/
  # docs_url: https://chemiscope.org/docs/
  github_id: lab-cosmo/chemiscope
  npm_id: chemiscope
- name: pyLODE
  category: rep-eng
  labels: ["electrostatics"]
  github_id: ceriottm/lode
- name: COSMO Toolbox
  category: math
  labels: ["lang-cpp"]
  github_id: lab-cosmo/toolbox
- name: COSMO tools
  github_id: lab-cosmo/cosmo-tools
  ignore: true
  # feeback from COSMO: intended for internal use
- name: M3GNet
  category: uip
  labels: ["ml-iap", "pretrained"]
  # homepage: https://crystals.ai/
  github_id: materialsvirtuallab/m3gnet
  pypi_id: m3gnet
- name: MEGNet
  category: ml-iap
  labels: ["multifidelity"]
  # homepage: https://crystals.ai/
  github_id: materialsvirtuallab/megnet
  pypi_id: megnet
- name: MatGL (Materials Graph Library)
  category: rep-learn
  labels: ["multifidelity"]
  # homepage: https://crystals.ai/
  github_id: materialsvirtuallab/matgl
  pypi_id: m3gnet
- name: MAML
  category: general-tool
  # homepage: https://materialsvirtuallab.github.io/maml/
  github_id: materialsvirtuallab/maml
  pypi_id: maml
- name: "Crystal Graph Convolutional Neural Networks (CGCNN)"
  category: rep-learn
  # homepage: https://crystals.ai/
  github_id: txie-93/cgcnn
- name: "GATGNN: Global Attention Graph Neural Network"
  category: rep-learn
  github_id: superlouis/GATGNN
- name: DeeperGATGNN
  category: rep-learn
  github_id: usccolumbia/deeperGATGNN
- name: MAST-ML
  category: general-tool
  github_id: uw-cmg/MAST-ML
- name: AMP
  resource:
  category: rep-eng
  # category: general-tool
  description: Amp is an open-source package designed to easily bring machine-learning to atomistic calculations.
  homepage: https://bitbucket.org/andrewpeterson/amp/
  docs_url: https://amp.readthedocs.io/
  pypi_id: amp-atomistics
- name: AMPtorch
  category: general-tool
  # docs_url: https://amptorch.readthedocs.io/
  github_id: ulissigroup/amptorch
- name: ASAP
  category: unsupervised
  # docs_url: https://bingqingcheng.github.io/
  github_id: BingqingCheng/ASAP
- name: AdsorbML
  category: rep-learn
  description:
  labels: ["surface-science", "single-paper"]
  # homepage: https://opencatalystproject.org/
  github_id: Open-Catalyst-Project/AdsorbML
- name: DeepDFT
  category: ml-dft
  # homepage: https://doi.org/10.1038/s41524-022-00863-y
  github_id: peterbjorgensen/DeepDFT
- name: SchNetPack
  category: rep-learn
  # homepage: http://quantum-machine.org/
  # docs_url: https://schnetpack.readthedocs.io/
  license: "MIT"
  github_id: atomistic-machine-learning/schnetpack
  pypi_id: schnetpack
- name: SchNetPack G-SchNet
  category: generative
  github_id: atomistic-machine-learning/schnetpack-gschnet
- name: cG-SchNet
  category: generative
  github_id: atomistic-machine-learning/cG-SchNet
- name: G-SchNet
  category: generative
  github_id: atomistic-machine-learning/G-SchNet
- name: SchNOrb
  category: ml-wft
  github_id: atomistic-machine-learning/SchNOrb
- name: FieldSchNet
  category: rep-learn
  license: "MIT"
  github_id: atomistic-machine-learning/field_schnet
  description: Deep neural network for molecules in external fields
- name: SchNet
  category: ml-iap
  github_id: atomistic-machine-learning/SchNet
- name: sGDML
  category: ml-iap
  # homepage: http://sgdml.org/
  # docs_url: http://docs.sgdml.org/
  github_id: stefanch/sGDML
  pypi_id: sgdml
- name: DTNN
  category: rep-learn
  github_id: atomistic-machine-learning/dtnn
- name: "Alchemical learning"
  category: ml-iap
  description: "Code for the 'Modeling high-entropy transition metal alloys with alchemical compression' article"
  github_id: Luthaf/alchemical-learning
- name: JAX-MD
  category: md
  github_id: jax-md/jax-md
  pypi_id: jax-md
- name: Finetuna
  category: active-learning
  # labels: ["active-learning"]
  github_id: ulissigroup/finetuna
- name: "Computational Autonomy for Materials Discovery (CAMD)"
  category: materials-discovery
  github_id: ulissigroup/CAMD
- name: CatGym
  category: reinforcement-learning
  license: "GPL"
  github_id: ulissigroup/catgym
- name: "uncertainty_benchmarking"
  category: general-tool
  labels: ["benchmarking", "probabilistic"]
  github_id: ulissigroup/uncertainty_benchmarking
- name: Automatminer
  category: general-tool
  labels: ["automl"]
  # homepage: https://hackingmaterials.lbl.gov/automatminer/
  license: https://github.com/hackingmaterials/automatminer/blob/main/LICENSE
  github_id: hackingmaterials/automatminer
  pypi_id: automatminer
- name: Matminer
  category: general-tool
  # homepage: https://hackingmaterials.github.io/matminer/
  license: https://github.com/hackingmaterials/matminer/blob/main/LICENSE
  github_id: hackingmaterials/matminer
  pypi_id: matminer
  conda_id: conda-forge/matminer
- name: JARVIS-Tools
  category: general-tool
  # docs_url: https://jarvis-tools.readthedocs.io/
  license: https://github.com/usnistgov/jarvis/blob/master/LICENSE.rst
  github_id: usnistgov/jarvis
  pypi_id: jarvis-tools
  conda_id: conda-forge/jarvis-tools
- name: ALIGNN
  category: rep-learn
  # homepage: https://jarvis.nist.gov/login?next=/jalignn/
  github_id: usnistgov/alignn
  license: https://github.com/usnistgov/alignn/blob/main/LICENSE.rst
  pypi_id: alignn
- name: SNAP
  category: ml-iap
  github_id: materialsvirtuallab/snap
- name: XenonPy
  category: general-tool
  # docs_url: https://xenonpy.readthedocs.io/
  github_id: yoshida-lab/XenonPy
  pypi_id: xenonpy
- name: Magpie
  category: general-tool
  description: Materials Agnostic Platform for Informatics and Exploration (Magpie)
  labels: ["lang-java"]
  homepage: https://bitbucket.org/wolverton/magpie/
  license: MIT
- name: QML
  category: general-tool
  # homepage: http://www.qmlcode.org/
  github_id: qmlcode/qml
  pypi_id: qml
- name: CBFV
  category: rep-eng
  github_id: kaaiian/CBFV
  pypi_id: CBFV
- name: DScribe
  category: rep-eng
  # docs_url: https://singroup.github.io/dscribe/
  github_id: SINGROUP/dscribe
  pypi_id: dscribe
  conda_id: conda-forge/dscribe
- name: milad
  category: rep-eng
  labels: ["generative"]
  license: GPL-3.0
  github_id: muhrin/milad
- name: e3psi
  category: ml-esm
  github_id: muhrin/e3psi
- name: SA-GPR
  category: rep-eng
  labels: ["lang-c"]
  github_id: dilkins/TENSOAP
- name: SALTED
  category: ml-dft
  github_id: andreagrisafi/SALTED
- name: Compositionally-Restricted Attention-Based Network (CrabNet)
  category: rep-learn
  # docs_url: https://crabnet.readthedocs.io/
  github_id: sparks-baird/CrabNet
  pypi_id: crabnet
- name: QUIP
  category: general-tool
  labels: ["md", "ml-iap", "rep-eng", "lang-fortran"]
  # homepage: https://libatoms.github.io/
  # docs_url: http://libatoms.github.io/QUIP/
  license: GPL-2.0
  github_id: libAtoms/QUIP
  pypi_id: quippy-ase
  dockerhub_id: libatomsquip/quip
- name: GAP
  category: ml-iap
  description: Gaussian Approximation Potential (GAP)
  homepage: https://libatoms.github.io/
  # docs_url: https://libatoms.github.io/GAP
  license: https://github.com/libAtoms/GAP/blob/main/LICENSE.md
  github_id: libAtoms/GAP
- name: testing-framework
  category: ml-iap
  description: "The purpose of this repository is to aid the testing of a large number of interatomic potentials for a variety of systems (materials or molecules)."
  labels: ["benchmarking"]
  github_id: libAtoms/testing-framework
- name: q-pac
  category: ml-esm
  labels: ["electrostatics"]
  license: "MIT"
  # description: "Python package mainly focused on the kernel Charge Equilibration method."
  gitlab_id: jmargraf/qpac
- name: kdft
  category: ml-dft
  # description: "The Kernel Density Functional (KDF) code allows generating ML based DFT functionals."
  gitlab_id: jmargraf/kdf
- name: CatLearn
  category: rep-eng
  description:
  labels: ["surface-science"]
  github_id: SUNCAT-Center/CatLearn
  pypi_id: catlearn
- name: FLARE
  category: active-learning
  labels: ["lang-cpp", "ml-iap"]
  # docs_url: https://mir-group.github.io/flare/
  github_id: mir-group/flare
- name: flare++
  category: active-learning
  labels: ["lang-cpp", "ml-iap"]
  # docs_url: https://mir-group.github.io/flare_pp
  github_id: mir-group/flare_pp
  pypi_id: flare_pp
- name: BOSS
  category: materials-discovery
  description: "Bayesian Optimization Structure Search (BOSS)"
  labels: ["probabilistic"]
  # docs_url: https://cest-group.gitlab.io/boss/
  gitlab_id: cest-group/boss
  pypi_id: aalto-boss
- name: ML-DFT
  category: ml-dft
  github_id: MihailBogojeski/ml-dft
- name: e3nn
  category: rep-learn
  # homepage: https://e3nn.org/
  # docs_url: https://docs.e3nn.org/
  license: "MIT"
  github_id: e3nn/e3nn
  conda_id: conda-forge/e3nn
  pypi_id: e3nn
- name: e3nn-jax
  category: rep-learn
  # homepage: https://e3nn.org/
  # docs_url: https://e3nn-jax.readthedocs.io/
  github_id: e3nn/e3nn-jax
  pypi_id: e3nn-jax
- name: Deep Graph Library (DGL)
  category: rep-learn
  # homepage: https://www.dgl.ai/
  # docs_url: https://docs.dgl.ai/
  github_id: dmlc/dgl
  pypi_id: dgl
  conda_id: dglteam/dgl
- name: dgl-lifesci
  resource:
  category: rep-learn
  # docs_url: https://lifesci.dgl.ai
  github_id: awslabs/dgl-lifesci
  pypi_id: dgllife
- name:  benchmarking-gnns
  category: rep-learn
  labels: ["single-paper", "benchmarking"]
  github_id: graphdeeplearning/benchmarking-gnns
- name: DeepErwin
  category: ml-wft
  # docs_url: https://mdsunivie.github.io/deeperwin/
  license: https://github.com/mdsunivie/deeperwin/blob/master/LICENSE
  github_id: mdsunivie/deeperwin
  pypi_id: deeperwin
- name: Allegro
  category: ml-iap
  github_id: mir-group/allegro
- name: NequIP
  category: ml-iap
  github_id: mir-group/nequip
  pypi_id: nequip
  conda_id: conda-forge/nequip
- name: kgcnn
  category: rep-learn
  # docs_url: https://kgcnn.readthedocs.io/
  github_id: aimat-lab/gcnn_keras
  pypi_id: kgcnn
- name: ML4pXRDs
  resource:
  category: rep-learn
  labels: ["xrd", "single-paper"]
  github_id: aimat-lab/ML4pXRDs
- name: "MEGAN: Multi Explanation Graph Attention Student"
  category: xai
  labels: ["rep-learn"]
  license: MIT
  github_id: aimat-lab/graph_attention_student
  # package managers: none
- name: NNsforMD
  resource:
  category: ml-iap
  github_id: aimat-lab/NNsForMD
  pypi_id: pyNNsMD
- name: PANNA
  category: ml-iap
  description: A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic environment descriptors and atomic potentials.
  labels: ["benchmarking"]
  # docs_url: https://pannadevs.gitlab.io/pannadoc/
  license: "MIT"
  gitlab_id: PANNAdevs/panna
- name: OpenChem
  category: general-tool
  # docs_url: https://mariewelt.github.io/OpenChem/
  github_id: Mariewelt/OpenChem
- name: ReLeaSE
  category: reinforcement-learning
  labels: ["drug-discovery"]
  github_id: isayev/ReLeaSE
- name: Libnxc
  resource:
  category: ml-dft
  labels: ["lang-cpp", "lang-fortran"]
  # docs_url: https://libnxc.readthedocs.io/
  github_id: semodi/libnxc
- name: NeuralXC
  category: ml-dft
  github_id: semodi/neuralxc
- name: ANI-1
  category: ml-iap
  github_id: isayev/ASE_ANI
- name: TorchANI
  category: ml-iap
  # docs_url: https://aiqm.github.io/torchani/
  github_id: aiqm/torchani
  pypi_id: torchani
  conda_id: conda-forge/torchani
- name: AIMNet
  category: ml-iap
  labels: ["single-paper"]
  github_id: aiqm/aimnet
- name: MALA
  category: ml-dft
  # docs_url: https://mala-project.github.io/mala/
  github_id: mala-project/mala
- name: MALADA
  category: ml-dft
  github_id: mala-project/malada
- name: rxngenerator
  category: generative
  github_id: tsudalab/rxngenerator
- name: bVAE-IM
  category: generative
  labels: ["qml", "single-paper"]
  github_id: tsudalab/bVAE-IM
- name: MolSLEPA
  category: generative
  labels: ["xai"]
  github_id: tsudalab/MolSLEPA
- name: MoLeR
  category: generative
  github_id: microsoft/molecule-generation
  pypi_id: molecule-generation
- name: paper-qa
  category: language-models
  labels: ["ai-agent"]
  github_id: whitead/paper-qa
  pypi_id: paper-qa
- name: MolSkill
  category: language-models
  labels: ["drug-discovery", "recommender"]
  github_id: microsoft/molskill
  conda_id: msr-ai4science/molskill
- name: synspace
  category: generative
  github_id: whitead/synspace
  pypi_id: synspace
- name: nlcc
  category: language-models
  labels: ["single-paper"]
  license: "MIT"
  github_id: whitead/nlcc
  pypi_id: nlcc
- name: SingleNN
  category: ml-iap
  description: "An efficient package for training and executing neural-network interatomic potentials"
  labels: ["lang-cpp"]
  github_id: lmj1029123/SingleNN
- name: PiNN
  category: ml-iap
  # docs_url: https://teoroo-cmc.github.io/PiNN
  github_id: Teoroo-CMC/PiNN
  dockerhub_id: teoroo/pinn
- name: "CCS_fit"
  category: ml-iap
  github_id: Teoroo-CMC/CCS
  pypi_id: ccs_fit
- name: RuNNer
  # resource: true
  category: ml-iap
  description: "The RuNNer Neural Network Energy Representation is a Fortran-based framework for the construction of Behler-Parrinello-type high-dimensional neural network potentials."
  labels: ["lang-fortran"]
  homepage: https://www.uni-goettingen.de/de/560580.html
  docs_url: https://theochemgoettingen.gitlab.io/RuNNer/
  license: "GPL-3.0"
- name: "charge_transfer_nnp"
  category: rep-learn
  labels: ["electrostatics"]
  github_id: pfnet-research/charge_transfer_nnp
- name: SIMPLE-NN
  category: ml-iap
  # docs_url: https://simple-nn-v2.readthedocs.io/
  github_id: MDIL-SNU/SIMPLE-NN
- name: "SIMPLE-NN v2"
  category: ml-iap
  # docs_url: https://simple-nn-v2.readthedocs.io/
  github_id: MDIL-SNU/SIMPLE-NN_v2
  description: SIMPLE-NN is an open package that constructs Behler-Parrinello-type neural-network interatomic potentials from ab initio data.
- name: SPINNER
  category: materials-discovery
  labels: ["lang-cpp", "structure-prediction"]
  # docs_url: https://spinner-csp.readthedocs.io/
  github_id: MDIL-SNU/SPINNER
- name: DeepChem
  category: general-tool
  # homepage: https://deepchem.io/
  # docs_url: https://deepchem.readthedocs.io/
  github_id: deepchem/deepchem
  pypi_id: deepchem
  conda_id: conda-forge/deepchem
  dockerhub_id: deepchemio/deepchem
- name: torchchem
  category: general-tool
  github_id: deepchem/torchchem
- name: JAXChem
  category: general-tool
  # docs_url: https://jaxchem.readthedocs.io/
  license: "MIT"
  github_id: deepchem/jaxchem
- name: DADApy
  category: unsupervised
  # docs_url: https://dadapy.readthedocs.io/
  github_id: sissa-data-science/DADApy
  pypi_id: dadapy
- name: "CSPML (crystal structure prediction with machine learning-based element substitution)"
  category: materials-discovery
  labels: ["structure-prediction"]
  github_id: minoru938/cspml
- name: KmdPlus
  category: unsupervised
  github_id: Minoru938/KmdPlus
- name: n2p2
  resource:
  category: ml-iap
  labels: ["lang-cpp"]
  # docs_url: https://compphysvienna.github.io/n2p2/
  github_id: CompPhysVienna/n2p2
- name: aviary
  category: materials-discovery
  github_id: CompRhys/aviary
- name: "SE(3)-Transformers"
  category: rep-learn
  labels: ["single-paper", "transformer"]
  license: "MIT"
  github_id: FabianFuchsML/se3-transformer-public
- name: "NVIDIA Deep Learning Examples for Tensor Cores"
  category: "rep-learn"
  labels: ["educational", "drug-discovery"]
  license: https://github.com/NVIDIA/DeepLearningExamples/blob/master/DGLPyTorch/DrugDiscovery/SE3Transformer/LICENSE
  homepage: https://github.com/NVIDIA/DeepLearningExamples#graph-neural-networks
  github_id: NVIDIA/DeepLearningExamples
- name: DeePMD-kit
  category: ml-iap
  labels: ["lang-cpp"]
  # docs_url: https://docs.deepmodeling.com/projects/deepmd/
  github_id: deepmodeling/deepmd-kit
  pypi_id: deepmd-kit
  conda_id: deepmodeling/deepmd-kit
  dockerhub_id: deepmodeling/deepmd-kit
- name: DP-GEN
  category: ml-iap
  labels: ["workflows"]
  # docs_url: https://docs.deepmodeling.com/projects/dpgen/
  github_id: deepmodeling/dpgen
  pypi_id: dpgen
  conda_id: deepmodeling/dpgen
- name: dpdata
  category: data-structures
  # docs_url: https://docs.deepmodeling.com/projects/dpdata/
  github_id: deepmodeling/dpdata
  pypi_id: dpdata
  conda_id: deepmodeling/dpdata
- name: DMFF
  category: ml-iap
  github_id: deepmodeling/DMFF
- name: DeePKS-kit
  category: ml-dft
  github_id: deepmodeling/deepks-kit
- name: Uni-Mol
  category: rep-learn
  labels: ["pretrained"]
  github_id: deepmodeling/Uni-Mol
- name: RDKit
  resource:
  category: general-tool
  description:
  labels: ["lang-cpp"]
  # homepage: https://www.rdkit.org/
  # docs_url: https://www.rdkit.org/docs/
  github_id: rdkit/rdkit
  pypi_id: rdkit
  conda_id: rdkit/rdkit
- name: "Neural Force Field"
  category: ml-iap
  labels: ["pretrained"]
  github_id: learningmatter-mit/NeuralForceField
- name: "atom_by_atom"
  category: rep-learn
  labels: ["surface-science", "single-paper"]
  github_id: learningmatter-mit/atom_by_atom
- name: Per-Site CGCNN
  category: rep-learn
  labels: ["pretrained", "single-paper"]
  github_id: learningmatter-mit/per-site_cgcnn
- name: Per-site PAiNN
  category: rep-learn
  labels: ["probabilistic", "pretrained", "single-paper"]
  github_id: learningmatter-mit/per-site_painn
- name: UVVisML
  category: rep-learn
  labels: ["optical-properties", "single-paper", "probabilistic"]
  github_id: learningmatter-mit/uvvisml
- name: "Atomistic Adversarial Attacks"
  category: ml-iap
  labels: ["probabilistic"]
  github_id: learningmatter-mit/Atomistic-Adversarial-Attacks
- name: Coarse-Graining-Auto-encoders
  category: unsupervised
  labels: ["single-paper"]
  github_id: learningmatter-mit/Coarse-Graining-Auto-encoders
  description: Implementation of coarse-graining Autoencoders
- name: GLAMOUR
  category: rep-learn
  labels: ["single-paper"]
  license: "MIT"
  github_id: learningmatter-mit/GLAMOUR
- name: DimeNet
  category: ml-iap
  # homepage: https://www.cs.cit.tum.de/daml/dimenet/
  license: https://github.com/gasteigerjo/dimenet/blob/master/LICENSE.md
  github_id: gasteigerjo/dimenet
- name: GemNet
  category: ml-iap
  # homepage: https://www.cs.cit.tum.de/daml/gemnet/
  license: https://github.com/TUM-DAML/gemnet_pytorch/blob/master/LICENSE
  github_id: TUM-DAML/gemnet_pytorch
- name: GDC
  category: rep-learn
  labels: ["generative"]
  github_id: gasteigerjo/gdc
- name: "Graph Transport Network"
  category: rep-learn
  labels: ["transport-phenomena"]
  license: https://github.com/gasteigerjo/gtn/blob/main/LICENSE.md
  github_id: gasteigerjo/gtn
- name: PyG Models
  category: rep-learn
  labels: ["general-ml"]
  description: Representation learning models implemented in PyTorch Geometric.
  homepage: https://github.com/pyg-team/pytorch_geometric/tree/master/torch_geometric/nn/models
  github_id: pyg-team/pytorch_geometric
- name: AGOX
  category: materials-discovery
  description: "AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional theory as the objective function."
  labels: ["structure-optimization"]
  # docs_url: https://agox.gitlab.io/agox/
  license: "GPL-3.0"
  gitlab_id: agox/agox
  pypi_id: agox
- name: gpax
  category: math
  labels: ["probabilistic", "active-learning"]
  # docs_url: http://gpax.rtfd.io/
  github_id: ziatdinovmax/gpax
  pypi_id: gpax
- name: "Neural fingerprint (nfp)"
  category: rep-learn
  license: https://github.com/NREL/nfp/blob/master/LICENSE
  github_id: NREL/nfp
- name: KLIFF
  category: ml-iap
  labels: ["probabilistic", "workflows"]
  # docs_url: https://kliff.readthedocs.io/
  github_id: openkim/kliff
  pypi_id: kliff
  conda_id: conda-forge/kliff
- name: PyXtalFF
  resource:
  category: ml-iap
  # docs_url: https://pyxtal-ff.readthedocs.io/
  license: "MIT"
  github_id: MaterSim/PyXtal_FF
  pypi_id: pyxtal_ff
- name: MLatom
  github_id: dralgroup/mlatom
  category: general-tool
  labels: ["uip", "ml-iap", "md", "ml-dft", "ml-esm", "transfer-learning", "active-learning", "spectroscopy", "structure-optimization"]
  # homepage: http://mlatom.com/
  # docs_url: https://xacs.xmu.edu.cn/docs/mlatom/
  license: MIT
  pypi_id: mlatom
- name: fplib
  category: rep-eng
  labels: ["lang-c", "single-paper"]
  github_id: zhuligs/fplib
- name: PhysNet
  category: ml-iap
  labels: ["electrostatics"]
  github_id: MMunibas/PhysNet
- name: BenchML
  category: rep-eng
  labels: ["benchmarking"]
  github_id: capoe/benchml
  pypi_id: benchml
- name: SOAPxx
  category: rep-eng
  description: A SOAP implementation.
  labels: ["lang-cpp"]
  github_id: capoe/soapxx
- name: matsciml
  category: rep-learn
  labels: ["workflows", "benchmarking"]
  github_id: IntelLabs/matsciml
- name: magnetism-prediction
  category: rep-eng
  labels: ["magnetism", "single-paper"]
  # homepage: https://doi.org/10.1038/s41598-023-30438-w
  github_id: dppant/magnetism-prediction
- name: quantum-structure-ml
  category: general-tool
  description: "Multi-class classification model for predicting the magnetic order of magnetic structures and a binary classification model for predicting the propogation vector of magnetic structures."
  labels: ["magnetism", "benchmarking"]
  # homepage: https://doi.org/10.1016/j.isci.2022.105192
  github_id: hgheiberger/quantum-structure-ml
- name: So3krates (MLFF)
  category: ml-iap
  github_id: thorben-frank/mlff
- name: "Descriptor Embedding and Clustering for Atomisitic-environment Framework (DECAF)"
  category: unsupervised
  description: "Provides a workflow to obtain clustering of local environments in dataset of structures."
  homepage: https://gitlab.mpcdf.mpg.de/klai/decaf
  # manual metrics as of 2023-06-12
  star_count: 2
  commit_count: 41
  contributor_count: 2
- name: FitSNAP
  category: md
  # docs_url: https://fitsnap.github.io/
  github_id: FitSNAP/FitSNAP
  conda_id: conda-forge/fitsnap3
- name: CGAT
  category: rep-learn
  github_id: hyllios/CGAT
- name: SCFNN
  category: rep-learn
  description: "Self-consistent determination of long-range electrostatics in neural network potentials"
  labels: ["lang-cpp", "electrostatics", "single-paper"]
  # homepage: https://doi.org/10.1038/s41467-022-29243-2
  github_id: andy90/SCFNN
- name: "SISSO++"
  category: rep-eng
  description: "C++ Implementation of SISSO with python bindings"
  # homepage: https://doi.org/10.21105/joss.03960
  labels: ["lang-cpp"]
  license: "Apache-2.0"
  gitlab_id: sissopp_developers/sissopp
- name: SISSO
  category: rep-eng
  labels: ["lang-fortran"]
  github_id: rouyang2017/SISSO
- name: gprep
  category: ml-dft
  labels: ["single-paper"]
  license: "MIT"
  gitlab_id: jmargraf/gprep
- name: QDF for molecule
  category: ml-esm
  description: "Quantum deep field: data-driven wave function, electron density generation, and energy prediction and extrapolation with machine learning"
  github_id: masashitsubaki/QuantumDeepField_molecule
- name: "molecularGNN_smiles"
  category: rep-learn
  github_id: masashitsubaki/molecularGNN_smiles
- name: graphite
  category: rep-learn
  license: "MIT"
  github_id: llnl/graphite
- name: DeepH-pack
  category: ml-dft
  labels: ["lang-julia"]
  # docs_url: https://deeph-pack.deepmodeling.com/
  github_id: mzjb/DeepH-pack
- name: xDeepH
  category: ml-dft
  labels: ["magnetism", "lang-julia"]
  github_id: mzjb/xDeepH
- name: DeepH-E3
  category: ml-dft
  description: "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian"
  labels: ["magnetism"]
  github_id: Xiaoxun-Gong/DeepH-E3
- name: CSNN
  category: ml-dft
  github_id: foxjas/CSNN
- name: "Ultra-Fast Force Fields (UF3)"
  category: ml-iap
  # docs_url: https://uf3.readthedocs.io/
  github_id: uf3/uf3
  pypi_id: uf3
- name: exmol
  category: xai
  github_id: ur-whitelab/exmol
  pypi_id: exmol
- name: gptchem
  category: language-models
  description: "Use GPT-3 to solve chemistry problems."
  # docs_url: https://gptchem.readthedocs.io/
  github_id: kjappelbaum/gptchem
  pypi_id: gptchem
- name: Equiformer
  category: rep-learn
  labels: ["transformer"]
  github_id: atomicarchitects/equiformer
- name: MLIP-3
  category: ml-iap
  description: "MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP)"
  labels: ["lang-cpp"]
  # homepage: https://doi.org/10.48550/arXiv.2304.13144
  license: "BSD-2-Clause"
  gitlab_id: ashapeev/mlip-3
- name: interface-lammps-mlip-3
  category: md
  description: "An interface between LAMMPS and MLIP (version 3)."
  license: "GPL-2.0"
  gitlab_id: ivannovikov/interface-lammps-mlip-3
- name: MLDensity
  category: ml-dft
  description: "Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations"
  github_id: StefanoSanvitoGroup/MLdensity
- name: BERT-PSIE-TC
  category: language-models
  labels: ["magnetism"]
  github_id: StefanoSanvitoGroup/BERT-PSIE-TC
- name: ALF
  category: ml-iap
  labels: ["active-learning"]
  license: https://github.com/lanl/ALF/blob/main/LICENSE
  github_id: lanl/alf
- name: hippynn
  category: rep-learn
  labels: ["workflows"]
  license: https://github.com/lanl/hippynn/blob/main/LICENSE.txt
  # docs_url: https://lanl.github.io/hippynn/
  github_id: lanl/hippynn
- name: "Point Edge Transformer"
  category: rep-learn
  description: "Smooth, exact rotational symmetrization for deep learning on point clouds"
  homepage: https://zenodo.org/record/7967079
  license: "CC-BY-4.0"
  # docs_url: https://arxiv.org/abs/2305.19302
- name: DeepQMC
  category: ml-wft
  # homepage: https://deepqmc.github.io/
  # docs_url: https://deepqmc.github.io/
  github_id: deepqmc/deepqmc
  pypi_id: deepqmc
- name: DM21
  category: ml-dft
  description: "This package provides a PySCF interface to the DM21 (DeepMind 21) family of exchange-correlation functionals described in the paper 'Pushing the Frontiers of Density Functionals by Solving the Fractional Electron Problem'."
  homepage: https://github.com/google-deepmind/deepmind-research/tree/master/density_functional_approximation_dm21
  github_id: google-deepmind/deepmind-research
- name: FermiNet
  category: ml-wft
  labels: ["transformer"]
  github_id: google-deepmind/ferminet
- name: KFAC-JAX
  category: math
  # docs_url: https://kfac-jax.readthedocs.io/
  github_id: google-deepmind/kfac-jax
  pypi_id: kfac-jax
- name: Cormorant
  category: rep-learn
  # homepage: https://doi.org/10.48550/arXiv.1906.04015
  license: https://github.com/risilab/cormorant/blob/master/LICENSE
  github_id: risilab/cormorant
- name: SphericalNet
  category: rep-learn
  github_id: risilab/SphericalNet
- name: Autobahn
  category: rep-learn
  github_id: risilab/Autobahn
- name: GElib
  category: math
  labels: ["lang-cpp"]
  # docs_url: https://risi-kondor.github.io/GElib/
  github_id: risi-kondor/GElib
- name: cnine
  category: math
  labels: ["lang-cpp"]
  docs_url: https://risi-kondor.github.io/cnine/
  github_id: risi-kondor/cnine
- name: mp-pyrho
  category: data-structures
  labels: ["ml-dft"]
  license: https://github.com/materialsproject/pyrho
  github_id: materialsproject/pyrho
  pypi_id: mp-pyrho
  description: Tools for re-griding volumetric quantum chemistry data for machine-learning purposes.
- name: tensorfieldnetworks
  category: rep-learn
  license: MIT
  github_id: tensorfieldnetworks/tensorfieldnetworks
  description: Rotation- and translation-equivariant neural networks for 3D point clouds.
- name: escnn
  category: rep-learn
  license: https://github.com/QUVA-Lab/escnn/blob/master/LICENSE
  github_id: QUVA-Lab/escnn
  pypi_id: escnn
- name: DeepCDP
  category: ml-dft
  # license: # none found
  github_id: siddarthachar/deepcdp
  # package managers: none
- name: ai4material_design
  category: rep-learn
  labels: ["pretrained", "material-defect"]
  license: Apache-2.0
  github_id: HSE-LAMBDA/ai4material_design
  # package managers: none
- name: escnn_jax
  category: rep-learn
  license: https://github.com/emilemathieu/escnn_jax/blob/master/LICENSE
  github_id: emilemathieu/escnn_jax
  # pypi_id: escnn_jax # listed in README, but not existing on PyPI
- name: CraTENet
  category: rep-learn
  labels: ["transport-phenomena"]
  license: MIT
  github_id: lantunes/CraTENet
  # package managers: none
- name: Atom2Vec
  category: rep-learn
  # license: # none found
  github_id: idocx/Atom2Vec
  pypi_id: atom2vec
- name: mat2vec
  category: language-models
  labels: ["rep-learn"]
  license: MIT
  github_id: materialsintelligence/mat2vec
  # package managers: none
- name: SkipAtom
  category: rep-eng
  license: MIT
  github_id: lantunes/skipatom
  pypi_id: skipatom
  conda_id: conda-forge/skipatom
- name: Artificial Intelligence for Science (AIRS)
  category: general-tool
  labels: ["rep-learn", "generative", "ml-iap", "md", "ml-dft", "ml-wft", "biomolecules"]
  license: GPL-3.0 license
  github_id: divelab/AIRS
  # package managers: none
- name: GlassPy
  category: rep-eng
  license: GPL-3.0
  github_id: drcassar/glasspy
  # alt. github_id: CitrineInformatics-ERD-public/glasspy
  pypi_id: glasspy
- name: sl_discovery
  category: materials-discovery
  labels: ["materials-discovery", "single-paper"]
  license: Apache-2.0
  github_id: CitrineInformatics-ERD-public/sl_discovery
  # package managers: none
- name: Linear vs blackbox
  category: xai
  labels: ["xai", "single-paper", "rep-eng"]
  # license: # none found
  github_id: CitrineInformatics-ERD-public/linear-vs-blackbox
- name: closed-loop-acceleration-benchmarks
  category: materials-discovery
  labels: ["materials-discovery", "active-learning", "single-paper"]
  license: MIT
  github_id: aced-differentiate/closed-loop-acceleration-benchmarks
  # package managers: none
- name: EquivariantOperators.jl
  category: math
  labels: ["lang-julia"]
  github_id: aced-differentiate/EquivariantOperators.jl
  license: MIT
  description: This package is deprecated. Functionalities are migrating to Porcupine.jl
- name: cdk
  github_id: cdk/cdk
  category: rep-eng
  labels: ["cheminformatics", "lang-java"]
  license: LGPL-2.1
  maven_id: org.openscience.cdk:cdk-bundle
- name: wfl
  category: ml-iap
  labels: ["workflows", "htc"]
  # license: # none found
  github_id: libAtoms/workflow
  # package managers: none
  description: Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows.
- name: ML-for-CurieTemp-Predictions
  category: rep-eng
  labels: ["single-paper", "magnetism"]
  license: MIT
  github_id: msg-byu/ML-for-CurieTemp-Predictions
  # package managers: none
  description: Machine Learning Predictions of High-Curie-Temperature Materials
- name: EDM
  description: E(3) Equivariant Diffusion Model for Molecule Generation in 3D.
  github_id: ehoogeboom/e3_diffusion_for_molecules
  category: generative
  license: MIT
  # package managers: none
- name: EquiformerV2
  category: rep-learn
  # license: # none found
  github_id: atomicarchitects/equiformer_v2
  # package managers: none
- name: GT4SD
  category: generative
  labels: ["pretrained", "drug-discovery", "rep-learn"]
  github_id: GT4SD/gt4sd-core
  license: MIT
  pypi_id: gt4sd
- name: OpenMM-ML
  category: md
  labels: ["ml-iap"]
  license: MIT
  github_id: openmm/openmm-ml
  conda_id: conda-forge/openmm-ml
- name: NNPOps
  category: ml-iap
  labels: ["md", "lang-cpp"]
  license: MIT
  github_id: openmm/NNPOps
  conda_id: conda-forge/nnpops
- name: openmm-torch
  category: md
  labels: ["ml-iap", "lang-cpp"]
  license: https://github.com/openmm/openmm-torch#license
  github_id: openmm/openmm-torch
  conda_id: conda-forge/openmm-torch
- name: CHGNet
  category: uip
  labels: ["ml-iap", "md", "pretrained", "electrostatics", "magnetism", "structure-relaxation"]
  license: https://github.com/CederGroupHub/chgnet/blob/main/LICENSE
  github_id: CederGroupHub/chgnet
  pypi_id: chgnet
- name: PROPhet
  category: ml-dft
  labels: ["ml-iap", "md", "single-paper", "lang-cpp"]
  license: GPL-3.0
  github_id: biklooost/PROPhet
  # package managers: none
- name: TensorMol
  category: ml-iap
  labels: ["single-paper"]
  license: GPL-3.0
  github_id: jparkhill/TensorMol
  # package managers: none
- name: Element encoder
  category: rep-learn
  labels: ["single-paper"]
  license: GPL-3.0
  github_id: jeherr/element-encoder
  # package managers: none
- name: MODNet
  category: rep-eng
  labels: ["pretrained", "small-data", "transfer-learning"]
  license: MIT
  github_id: ppdebreuck/modnet
  # homepage: https://modnet.readthedocs.io/
- name: mlcolvar
  category: md
  labels: ["sampling"]
  license: MIT
  # docs_url: https://mlcolvar.readthedocs.io/
  github_id: luigibonati/mlcolvar
  pypi_id: mlcolvar
- name: mlp
  description: "Proper orthogonal descriptors for efficient and accurate interatomic potentials. [Paper](https://doi.org/10.1016/j.jcp.2023.112030)."
  github_id: cesmix-mit/mlp
  category: ml-iap
  labels: ["lang-julia"]
  # license: # none found
  # package managers: none
- name: COATI
  github_id: terraytherapeutics/COATI
  category: generative
  labels: ["drug-discovery", "multimodal", "pretrained", "rep-learn"]
  license: Apache 2.0
  # package managers: none
- name: ML-DFT
  github_id: Ramprasad-Group/ML-DFT
  category: ml-dft
  license: https://github.com/Ramprasad-Group/ML-DFT/blob/main/LICENSE
  # package managers: none
- name: Mat2Spec
  github_id: gomes-lab/Mat2Spec
  category: ml-dft
  labels: ["spectroscopy"]
  license: MIT
  # package managers: none
  description: Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings
- name: APET
  github_id: emotionor/APET
  category: ml-dft
  labels: ["density-of-states", "transformer"]
  license: GPL-3.0
  # package managers: none
- name: JAX-DFT
  homepage: https://github.com/google-research/google-research/tree/master/jax_dft
  github_id: google-research/google-research
  category: ml-dft
  license: Apache-2.0
  # package managers: none
  description: This library provides basic building blocks that can construct DFT calculations as a differentiable program.
- name: pretrained-gnns
  github_id: snap-stanford/pretrain-gnns
  category: rep-learn
  labels: ["pretrained"]
  license: MIT
  # package managers: none
- name: A3MD
  github_id: brunocuevas/a3md
  category: ml-dft
  labels: ["rep-learn", "single-paper"]
  # license: # none found
  # package managers: none
- name: torch_spex
  github_id: lab-cosmo/torch_spex
  category: math
  # license: # none found
  # package managers: none
- name: tinker-hp
  github_id: TinkerTools/tinker-hp
  category: ml-iap
  license: https://github.com/TinkerTools/tinker-hp/blob/master/license-Tinker.pdf
  # package managers: none
- name: Grad DFT
  github_id: XanaduAI/GradDFT
  category: ml-dft
  license: Apache-2.0
  # package managers: none
- name: Geometric GNN Dojo
  github_id: chaitjo/geometric-gnn-dojo
  category: rep-learn
  labels: ["benchmarking", "transformer"]
  license: MIT
  # package managers: none
- name: ChemCrow
  github_id: ur-whitelab/chemcrow-public
  category: language-models
  labels: ["ai-agent"]
  license: MIT
  pypi_id: chemcrow
  description: Open source package for the accurate solution of reasoning-intensive chemical tasks.
- name: MoLFormer
  github_id: IBM/molformer
  category: language-models
  labels: ["transformer", "pretrained", "drug-discovery"]
  license: Apache-2.0
  # package managers: none
- name: QHNet
  homepage: https://github.com/divelab/AIRS/tree/main/OpenDFT/QHNet
  github_id: divelab/AIRS
  category: ml-dft
  labels: ["rep-learn"]
  license: GPL-3.0
  # package managers: none
- name: "DIG: Dive into Graphs"
  github_id: divelab/DIG
  category: rep-learn
  license: GPL-3.0
  pypi_id: dive-into-graphs
- name: ChemDataExtractor
  github_id: mcs07/ChemDataExtractor
  category: language-models
  labels: ["literature-data"]
  license: MIT
  pypi_id: chemdataextractor
  conda_id: chemdataextractor/chemdataextractor
- name: ChemDataWriter
  github_id: ShuHuang/chemdatawriter
  category: language-models
  labels: ["literature-data"]
  license: MIT
  # pypi_id: chemdatawriter # readme says it's available, PyPI says it's not.
- name: T-e3nn
  github_id: Hongyu-yu/T-e3nn
  category: rep-learn
  labels: ["magnetism"]
  license: MIT
  # package managers: none
- name: pair_allegro
  github_id: mir-group/pair_allegro
  category: md
  labels: ["ml-iap", "rep-learn"]
  license: MIT
  # package managers: none
- name: MAPI_LLM
  github_id: maykcaldas/MAPI_LLM
  category: language-models
  labels: ["ai-agent", "dataset"]
  license: MIT
  # package managers: none
  description: "A LLM application developed during the LLM March MADNESS Hackathon https://doi.org/10.1039/D3DD00113J"
- name: Allegro-JAX
  description: JAX implementation of the Allegro interatomic potential
  github_id: mariogeiger/allegro-jax
  category: ml-iap
  # license: # none found
  # package managers: none
- name: NequIP-JAX
  description: JAX implementation of the NequIP interatomic potential
  github_id: mariogeiger/nequip-jax
  category: ml-iap
  # license: # none found
  # package managers: none
- name: pair_nequip
  description: LAMMPS pair style for NequIP
  github_id: mir-group/pair_nequip
  category: md
  labels: ["ml-iap", "rep-learn"]
  license: MIT
  # package managers: none
- name: lie-nn
  github_id: lie-nn/lie-nn
  category: math
  labels: ["rep-learn"]
  license: MIT
  # package managers: none
- name: MEGNetSparse
  github_id: HSE-LAMBDA/MEGNetSparse
  category: ml-iap
  labels: ["material-defect"]
  license: MIT
  pypi_id: MEGNetSparse
- name: chemlift
  github_id: lamalab-org/chemlift
  category: language-models
  license: MIT
  # package managers: none
- name: OpenBioML ChemNLP
  github_id: OpenBioML/chemnlp
  category: language-models
  labels: ["datasets"]
  license: MIT
  pypi_id: chemnlp
- name: CatBERTa
  github_id: hoon-ock/CatBERTa
  category: language-models
  labels: ["transformer", "catalysis"]
  # license: # none found
  # package managers: none
- name: Point Edge Transformer (PET)
  github_id: spozdn/pet
  category: ml-iap
  labels: ["rep-learn", "transformer"]
  license: MIT
  # package managers: none
- name: TurboGAP
  github_id: mcaroba/turbogap
  category: ml-iap
  labels: ["lang-fortran"]
  license: https://github.com/mcaroba/turbogap/blob/master/LICENSE.md
  # package managers: none
- name: soap_turbo
  github_id: libAtoms/soap_turbo
  category: rep-eng
  labels: ["lang-fortran"]
  license: https://github.com/libAtoms/soap_turbo/blob/master/LICENSE.md
  # package managers: none
  description: "soap_turbo comprises a series of libraries to be used in combination with QUIP/GAP and TurboGAP"
- name: PeriodicPotentials
  github_id: AaltoRSE/PeriodicPotentials
  category: ml-iap
  labels: ["community", "visualization", "lang-js"]
  license: MIT
  # package managers: none
- name: charge-density-models
  github_id: ulissigroup/charge-density-models
  category: ml-dft
  labels: ["rep-learn"]
  license: MIT
  # package managers: none
  description: Tools to build charge density models using [fairchem](https://github.com/FAIR-Chem/fairchem)
- name: SciBot
  github_id: CFN-softbio/SciBot
  category: language-models
  labels: ["ai-agent"]
  # license: # none found
  # package managers: none
- name: dlpack
  github_id: dmlc/dlpack
  category: data-structures
  labels: ["lang-cpp"]
  license: Apache-2.0
  # package managers: none
- name: FAENet
  github_id: vict0rsch/faenet
  category: rep-learn
  license: MIT
  pypi_id: faenet
  description: Frame Averaging Equivariant GNN for materials modeling
- name: "Materials Discovery: GNoME"
  github_id: google-deepmind/materials_discovery
  category: materials-discovery
  labels: ["uip", "datasets", "rep-learn", "proprietary"]
  license: Apache-2.0
  show: true # hack, was hidden, metrics indicate otherwise
  # package managers: none
  description: "Graph Networks for Materials Science (GNoME) and dataset of 381,000 novel stable materials."
- name: halex
  github_id: ecignoni/halex
  category: ml-esm
  labels: ["excited-states"]
  # license: # none found
  # package managers: nonePackage Managers: None
  description: "Hamiltonian Learning for Excited States https://doi.org/10.48550/arXiv.2311.00844"
- name: pymatviz
  github_id: janosh/pymatviz
  category: visualization
  labels: ["general-tool", "probabilistic"]
  license: MIT
  pypi_id: pymatviz
- name: TorchMD-NET
  github_id: torchmd/torchmd-net
  category: ml-iap
  labels: ["md", "rep-learn", "transformer", "pretrained"]
  license: MIT
  conda_id: conda-forge/torchmd-net
- name:  paper-ml-robustness-material-property
  github_id: mathsphy/paper-ml-robustness-material-property
  category: unsupervised
  labels: ["datasets", "single-paper"]
  license:  BSD-3-Clause
  # package managers: none
  description: A critical examination of robustness and generalizability of machine learning prediction of materials properties
- name: LLM-Prop
  github_id: vertaix/LLM-Prop
  category: language-models
  # license: # none found
  # package managers: none
- name: MLXDM
  github_id: RowleyGroup/MLXDM
  category: ml-iap
  labels: ["long-range"]
  license: MIT
  # package managers: none
  description: "A Neural Network Potential with Rigorous Treatment of Long-Range Dispersion https://doi.org/10.1039/D2DD00150K"
- name: GPUMD
  github_id: brucefan1983/GPUMD
  category: ml-iap
  labels: ["md", "lang-cpp", "electrostatics"]
  license: GPL-3.0
  # package managers: none
  description: "GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials called neuroevolution potentials (NEPs)"
- name: calorine
  gitlab_id: materials-modeling/calorine
  category: ml-iap
  license: https://gitlab.com/materials-modeling/calorine/-/blob/master/LICENSE
  pypi_id: calorine
  description: A Python package for constructing and sampling neuroevolution potential models. https://doi.org/10.21105/joss.06264
- name: PyNEP
  github_id: bigd4/PyNEP
  category: ml-iap
  license: MIT
  description: A python interface of the machine learning potential NEP used in GPUMD.
- name: SOMD
  github_id: initqp/somd
  category: md
  labels: ["ml-iap", "active-learning"]
  license: AGPL-3.0
  # package managers: none
- name: apax
  category: ml-iap
  license: "MIT"
  github_id: apax-hub/apax
  pypi_id: apax
- name: LLaMP
  github_id: chiang-yuan/llamp
  category: language-models
  labels: ["materials-discovery", "cheminformatics", "generative", "MD", "multimodal", "language-models", "lang-py", "general-tool"]
  license: BSD-3-Clause
- name: IPSuite
  github_id: zincware/IPSuite
  category: active-learning
  license: "EPL-2.0"
  labels: ["ml-iap", "md", "workflows", "htc", "FAIR"]
  pypi_id: ipsuite
  description: A Python toolkit for FAIR development and deployment of machine-learned interatomic potentials.
- name: ZnDraw
  github_id: zincware/ZnDraw
  # homepage: https://zndraw.icp.uni-stuttgart.de
  category: visualization
  labels: ["md", "generative", "lang-js"]
  license: "EPL-2.0"
  pypi_id: zndraw
  description: A powerful tool for visualizing, modifying, and analysing atomistic systems.
- name: HydraGNN
  github_id: ORNL/HydraGNN
  category: rep-learn
  license:  BSD-3
  # package managers: none
- name: MatterGen
  resource: true
  homepage: https://www.microsoft.com/en-us/research/blog/mattergen-property-guided-materials-design/
  description: A generative model for inorganic materials design https://doi.org/10.48550/arXiv.2312.03687
  category: materials-discovery
  labels: ["generative", "proprietary"]
  # license: # none found
  # package managers: none
- name: ACEpsi.jl
  github_id: ACEsuit/ACEpsi.jl
  category: ml-wft
  labels: ["rep-eng", "lang-julia"]
  license: MIT
  # package managers: none
- name: MAGUS
  gitlab_id: bigd4/magus
  category: materials-discovery
  labels: ["structure-prediction", "active-learning"]
  # license: # none found
  # package managers: none
  description: Machine learning And Graph theory assisted Universal structure Searcher
- name: HamGNN
  github_id: QuantumLab-ZY/HamGNN
  category: ml-dft
  labels: ["rep-learn", "magnetism", "lang-c"]
  license: GPL-3.0
  # package managers: none
- name: MACE-MP
  description: Pretrained foundation models for materials chemistry
  github_id: ACEsuit/mace-mp
  category: uip
  labels: ["ml-iap", "pretrained", "rep-learn", "md"]
  license: MIT
  pypi_id: mace-torch
- name: QMLearn
  description: Quantum Machine Learning by learning one-body reduced density matrices in the AO basis. https://doi.org/10.1038/s41467-023-41953-9
  gitlab_id: pavanello-research-group/qmlearn
  category: ml-esm
  homepage: http://qmlearn.rutgers.edu/
  license: MIT
  # package managers: none
  # Note. Documentation not linked in repo README at time of writing. So, making an exception and adding it here as homepage.
- name: rho_learn
  github_id: jwa7/rho_learn
  category: ml-dft
  license: MIT
  # package managers: none
- name: Q-stack
  github_id: lcmd-epfl/Q-stack
  category: ml-dft
  labels: ["excited-states", "general-tool"]
  license: MIT
  # package managers: none
- name: KSR-DFT
  github_id: pedersor/ksr_dft
  category: ml-dft
  # labels: # none
  license: Apache-2.0
  # package managers: none
- name: Allegro-Legato
  description: An extension of Allegro with enhanced robustness and time-to-failure.
  github_id: ibayashi-hikaru/allegro-legato
  category: ml-iap
  labels: ["md"]
  license: MIT
  # package managers: none
- name: mag-ace
  description: Magnetic ACE potential. FORTRAN interface for LAMMPS SPIN package.
  github_id: mttrin93/mag-ace
  category: ml-iap
  labels: ["magnetism", "md", "lang-fortran"]
  # license: # none found
  # package managers: none
- name: crystal-text-llm
  github_id: facebookresearch/crystal-text-llm
  category: language-models
  labels: ["materials-discovery"]
  license: CC-BY-NC-4.0
  # package managers: none
- name: EGraFFBench
  github_id: M3RG-IITD/MDBENCHGNN
  category: rep-learn
  labels: ["single-paper", "benchmarking", "ml-iap"]
  # license: # none found (file exists but empty)
  # package managers: none
- name: automl-materials
  github_id: mm-tud/automl-materials
  category: rep-eng
  labels: ["automl", "benchmarking", "single-paper"]
  license: MIT
  # package managers: none
- name: ML-atomate
  github_id: takahashi-akira-36m/ml_atomate
  category: materials-discovery
  labels: ["active-learning", "workflows"]
  license: GPL-3.0
  # package managers: none
- name: ChargE3Net
  github_id: AIforGreatGood/charge3net
  category: ml-dft
  labels: ["rep-learn"]
  license: MIT
  # package managers: none
- name: InfGCN for Electron Density Estimation
  github_id: ccr-cheng/infgcn-pytorch
  category: ml-dft
  labels: ["rep-learn", "neural-operator"]
  license: MIT
  # package managers: none
- name: MXenes4HER
  description: Predicting hydrogen evolution (HER) activity over 4500 MXene materials https://doi.org/10.1039/D3TA00344B.
  github_id: cnislab/MXenes4HER
  category: rep-eng
  labels: ["materials-discovery", "catalysis", "scikit-learn", "single-paper"]
  license: GPL-3.0
  # package managers: none
- name: Mapping out phase diagrams with generative classifiers
  github_id: arnoldjulian/Mapping-out-phase-diagrams-with-generative-classifiers
  category: generative
  labels: ["phase-transition"]
  license: MIT
  # package managers: none
- name: ChatMOF
  github_id: Yeonghun1675/ChatMOF
  category: language-models
  labels: ["generative"]
  license: MIT
  pypi_id: chatmof
- name: PMTransformer
  github_id: hspark1212/MOFTransformer
  category: generative
  labels: ["transfer-learning", "pretrained", "transformer"]
  license: MIT
  pypi_id: moftransformer
- name: PyFLAME
  gitlab_id: flame-code/PyFLAME
  description: An automated approach for developing neural network interatomic potentials with FLAME https://doi.org/10.1016/j.commatsci.2021.110567
  category: ml-iap
  labels: ["active-learning", "structure-prediction", "structure-optimization", "rep-eng", "lang-fortran"]
  # license: # none found
  # package managers: none
- name: IKS-PIML
  description: Code and generated data for the paper "Inverting the Kohn-Sham equations with physics-informed machine learning" https://doi.org/10.1088/2632-2153/ad3159
  resource: true
  homepage: https://rodare.hzdr.de/record/2720
  category: ml-dft
  labels: ["neural-operator", "pinn", "datasets", "single-paper"]
  # license: # none found
  # package managers: none
- name: Graphormer
  github_id: microsoft/Graphormer
  category: rep-learn
  labels: ["transformer", "pretrained"]
  license: MIT
  # package managers: none
- name: Joint Multidomain Pre-Training (JMP)
  # homepage: https://nima.sh/jmp/
  github_id: facebookresearch/JMP
  category: uip
  labels: ["pretrained", "ml-iap", "general-tool"]
  license: CC-BY-NC-4.0
  # package managers: none
- name: AisNet
  description: A Universal Interatomic Potential Neural Network with Encoded Local Environment Features https://doi.org/10.1021/acs.jcim.3c00077
  github_id: loilisxka/AisNet
  category: ml-iap
  license: MIT
  # package managers: none
- name: DPA-2
  description: Towards a universal large atomic model for molecular and material simulation https://doi.org/10.48550/arXiv.2312.15492
  github_id: deepmodeling/deepmd-kit
  # homepage: https://github.com/deepmodeling/deepmd-kit/discussions/3772
  category: uip
  labels: ["ml-iap", "pretrained", "workflows", "datasets"]
  # license: # none found
  # package managers: none
- name: nnp-pre-training
  description: Synthetic pre-training for neural-network interatomic potentials
  github_id: jla-gardner/nnp-pre-training
  category: ml-iap
  labels: ["pretrained", "md"]
  # license: # none found
  # package managers: none
- name: SiMGen
  github_id: RokasEl/simgen
  category: generative
  labels: ["visualization"]
  license: MIT
  pypi_id: simgen
- name: TeaNet
  homepage: https://doi.org/10.24433/CO.0749085.v1
  resource: true
  description: Universal neural network interatomic potential inspired by iterative electronic relaxations https://doi.org/10.1016/j.commatsci.2022.111280
  category: uip
  labels: ["ml-iap"]
  # license: # none found
  # package managers: none
- name: PreFerred Potential (PFP)
  resource: true
  homepage: https://www.nature.com/articles/s41467-022-30687-9#code-availability
  description: Universal neural network potential for material discovery https://doi.org/10.1038/s41467-022-30687-9
  category: uip
  labels: ["ml-iap", "proprietary"]
  # license: # none found
  # package managers: none
- name: GN-MM
  description: "The Gaussian Moment Neural Network (GM-NN) package developed for large-scale atomistic simulations employing
atomistic neural networks based on Gaussian Moments"
  gitlab_id: zaverkin_v/gmnn
  category: ml-iap
  labels: ["active-learning", "md", "rep-eng", "magnetism"]
  license: MIT
  # package managers: none
- name: apax
  github_id: apax-hub/apax
  category: ml-iap
  labels: ["md"]
  license: MIT
  pypi_id: apax
- name: ALEBREW
  github_id: nec-research/alebrew
  category: active-learning
  labels: ["ml-iap", "md"]
  license: https://github.com/nec-research/alebrew/blob/main/LICENSE.txt
  # package managers: none
- name: Cephalo
  description: Multimodal Vision-Language Models for Bio-Inspired Materials Analysis and Design
  github_id: lamm-mit/Cephalo
  category: language-models
  labels: ["generative", "multimodal", "pretrained"]
  license: Apache-2.0
  # package managers: none
- name: SevenNet
  github_id: MDIL-SNU/SevenNet
  category: uip
  labels: ["ml-iap", "md", "pretrained"]
  license: GPL-3.0
  # package managers: none
  description: SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that supports parallel molecular dynamics simulations with LAMMPS.
- name: MatterSim
  resource: true
  homepage: https://www.microsoft.com/en-us/research/blog/mattersim-a-deep-learning-model-for-materials-under-real-world-conditions/
  description: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures https://doi.org/10.48550/arXiv.2405.04967
  category: uip
  labels: ["ml-iap", "active-learning", "proprietary"]
  # license: # none found
  # package managers: none
- name: Awesome-Crystal-GNNs
  github_id: kdmsit/Awesome-Crystal-GNNs
  category: community
  license: MIT
  # package managers: none
- name: Asparagus
  github_id: MMunibas/Asparagus
  category: ml-iap
  labels: ["workflows", "sampling", "md"]
  license: MIT
  # package managers: none
  description: Program Package for Sampling, Training and Applying ML-based Potential models https://doi.org/10.48550/arXiv.2407.15175
- name: AtomGPT
  github_id: usnistgov/atomgpt
  category: language-models
  labels: ["generative", "pretrained", "transformer"]
  license: https://github.com/usnistgov/atomgpt/blob/main/LICENSE.rst
  pypi_id: atomgpt
- name: NIST ChemNLP
  github_id: usnistgov/chemnlp
  category: language-models
  labels: ["literature-data"]
  license: MIT
  pypi_id: chemnlp
- name: Atomvision
  github_id: usnistgov/atomvision
  category: visualization
  labels: ["computer-vision", "experimental-data", "rep-learn"]
  license: https://github.com/usnistgov/atomvision/blob/master/LICENSE.md
  pypi_id: atomvision
- name: Crystal Toolkit
  github_id: materialsproject/crystaltoolkit
  category: visualization
  # labels: none
  license: MIT
  pypi_id: crystal-toolkit
- name: Elementari
  github_id: janosh/elementari
  category: visualization
  labels: ["lang-js"]
  license: MIT
  npm_id: elementari
- name: Orb Models
  github_id: orbital-materials/orb-models
  category: uip
  labels: ["ml-iap", "pretrained"]
  license: https://github.com/orbital-materials/orb-models/blob/main/LICENSE
  pypi_id: orb-models
- name: MLIP Arena Leaderboard
  homepage: https://huggingface.co/spaces/atomind/mlip-arena
  github_id: atomind-ai/mlip-arena
  category: uip
  labels: ["ml-iap", "community"]
  license: Apache-2.0
  # pypi_id: None
- name: GRACE
  github_id: ICAMS/grace-tensorpotential
  category: uip
  labels: ["ml-iap", "pretrained", "md", "rep-learn", "rep-eng"]
  license: https://github.com/ICAMS/grace-tensorpotential/blob/master/LICENSE.md
  # pypi_id: None
- name: ACE / GRACE support
  resource: true
  category: community
  homepage: https://acesupport.zulipchat.com
  # group: ACE
  description: Support forum for the Atomic Cluster Expansion (ACE) and extensions.
- name: MLIPX - Machine-Learned Interatomic Potential eXploration
  category: ml-iap
  labels: ["benchmarking", "visualization", "workflows"]
  license: MIT
  github_id: basf/mlipx
  pypi_id: mlipx
  # homepage: https://mlipx.readthedocs.io/