SoftwareFramework

HydraGNN

HydraGNN is a framework maintained by Oak Ridge National Laboratory1 2. Its source code is public1 2 under the BSD 3-Clause License2.

HydraGNN is a PyTorch implementation of multi-headed graph neural networks from Oak Ridge National Laboratory, with separate output heads for graph-level and node-level properties. Its README lists distributed training with DDP, FSDP, and DeepSpeed; equivariant layers such as EGNN, PaiNN, MACE, and DimeNet; heterogeneous graph learning; global attention through GPS; and training of machine-learned interatomic potentials with energy-conserving forces.1Fact reviewed Oct 1, 2026

Repository: Repository (ORNL/HydraGNN) (external site: github.com)
Documentation: User manual (external site: github.com)
License: LICENSE (BSD 3-Clause) (external site: github.com)
Release notes: Releases (external site: github.com)

Last reviewedOct 1, 2026Entry updated Oct 1, 2026Documented release Unknown

Availability and license

Overall availability

Public

Source code is public on GitHub and installed from source with pip after installing the listed dependencies.1

Availability fact review: Oct 1, 2026

Availability is separate from permission: read the license before using or redistributing.

BSD 3-Clause License (external site: github.com)2
Applies to: Code · SPDX BSD-3-Clause · on the rubric’s OSI-approved software-license list · fact reviewed Oct 1, 2026
Plain-language guide to the BSD 3-Clause License

Component reuse rights

A readable or downloadable component is not automatically reusable. These indicators concern recorded license evidence, not system certification.

weights: Unknown — no complete fact-level rights review
code: Reviewed qualifying license recorded — check scope and conditions
data: Unknown — no complete fact-level rights review
documentation: Unknown — no complete fact-level rights review

No complete system-rights review is recorded for this release.

Public materials checklist

Items for a framework under USASI rubric v0.2. Unknown means unassessed or insufficient evidence.

Public materials checklist for HydraGNN
Item	Status	Notes and evidence
Source codeIs the source code publicly readable?	Public	Public repository in ORNL's GitHub organization under the BSD 3-Clause License.1 2
DocumentationIs user documentation published?	Public	The README covers capabilities, dependencies, installation, a quick start, and configuration settings, and links to a user manual, feature guides, and a project wiki.1
InstallationAre installation instructions or packages publicly available?	Public	Dependencies are installed with an included script or modular pip requirement files (core, PyTorch, PyTorch Geometric, optional extras); HydraGNN itself is installed with pip from the cloned repository.1
Supported platformsAre supported operating systems or hardware documented?	Partial	The README states that HydraGNN is tested on Python 3.11 through 3.14, accepts PyTorch 2.13 or 2.14, and ships installation assets for HPC facilities. No operating-system or accelerator support matrix was found.1
Release statusAre versioned releases published?	Public	Versioned GitHub releases include v4.0 (August 15, 2025) and v5.0 (April 14, 2026).3

What it is useful for

Training graph neural network surrogate models on atomistic and materials data, such as density functional theory outputs, including distributed training at supercomputing scale. The README also documents constraint-aware training for optimal power flow problems and gives example scripts for each workflow.1Fact reviewed Oct 1, 2026

Run and use notes

Documented facts only. No hardware or performance claims are made without a cited source and stated assumptions.

The README's baseline dependency script uses NumPy 2.4.6, PyTorch 2.13.0, torchvision 0.28.0, and PyTorch Geometric 2.8.0. The pyg-lib dependency is built from source, which requires a working C++ compiler.1

Organization context

Oak Ridge National Laboratory

U.S. eligibility

Project eligibility rests on documented governing or maintaining entities, not on contributors.

Eligible · basis: U.S.-governed project

HydraGNN is published in Oak Ridge National Laboratory's ORNL GitHub organization, and its BSD 3-Clause license names Oak Ridge National Laboratory as copyright holder. ORNL is a U.S. Department of Energy national laboratory in Oak Ridge, Tennessee, managed by UT-Battelle LLC for DOE.1 2 4

Assessed Oct 1, 2026

Sources

1.
ORNL/HydraGNN (GitHub repository and README) (external site: github.com)
Oak Ridge National Laboratory · Repository · accessed Oct 1, 2026 · evidence reviewed Oct 1, 2026
2.
HydraGNN LICENSE (BSD 3-Clause) (external site: raw.githubusercontent.com)
Oak Ridge National Laboratory · License · accessed Oct 1, 2026 · evidence reviewed Oct 1, 2026
3.
Releases · ORNL/HydraGNN (external site: github.com)
Oak Ridge National Laboratory · Release notes · accessed Oct 1, 2026 · evidence reviewed Oct 1, 2026
4.
About Us | ORNL (external site: ornl.gov)
Oak Ridge National Laboratory · Official page · accessed Oct 1, 2026 · evidence reviewed Oct 1, 2026

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This listing is not an endorsement, a safety assessment, or a federal approval.

Cite this page

APA style

USASI. (2026, October 1). HydraGNN. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/hydragnn/

BibTeX

@misc{usasi_hydragnn,
  author = {{USASI}},
  title = {HydraGNN},
  year = {2026},
  month = oct,
  howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/hydragnn/}},
  note = {United States of America Superintelligence. Last updated 2026-10-01}
}

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