hoomd-tf
The facts below are what the maker publishes about hoomd-tf, checked and dated. Our editors have not written about it yet.
The facts
Each one read from hoomd-tf's own pages, with the date it was checked.
What it does
- Assessment tools
- Nochecked 23 Sep 2026
- Built-in utilities
- Built-in utilities include RDF calculation, EDS biasing, and coarse-grained simulation force matching.checked 12 Oct 2026
- Early termination
- It supports online machine learning with early termination during molecular dynamics simulations.checked 12 Oct 2026
- GPU execution
- The project is designed to reduce latency with GPU-to-GPU communication between GPU-accelerated HOOMD-blue and TensorFlow.checked 5 Oct 2026
- GPU performance
- The project says it uses GPU-to-GPU communication to minimize latency between GPU-accelerated HOOMD-blue and TensorFlow.checked 12 Oct 2026
- GPU setup limit
- The guide warns that CUDA version mismatches between TensorFlow and HOOMD-blue can complicate GPU setup and recommends compiling HOOMD-blue for GPU use.checked 12 Oct 2026
- Integration
- The documented installation requires Python, TensorFlow, and HOOMD-blue.checked 12 Oct 2026
- Known compatibility constraint
- The guide says TensorFlow and HOOMD must use matching CUDA toolkit versions for GPU use, and recommends compiling HOOMD-blue for GPU mode because of CUDA version mismatches on conda.checked 5 Oct 2026
- Latest documented release
- The changelog lists version 2.4, dated 2021-07-26, with the ability to compute mapped neighbor lists and positions while a simulation runs.checked 5 Oct 2026
- Lesson library
- Nochecked 23 Sep 2026
- Memory control
- For models without molecule batching, tfcompute.attach accepts batch_size to split work into smaller batches for simulations with limited GPU memory.checked 5 Oct 2026
- Model inputs
- The TensorFlow interface exposes HOOMD-blue neighbor lists and particle positions to models.checked 12 Oct 2026
- Model interface
- Users build Keras models as SimModel subclasses, with particle neighbor lists, positions, and box data available as tensors.checked 5 Oct 2026
- Molecule batching
- MolSimModel can arrange positions, neighbor lists, and forces by molecule, including molecules of different sizes.checked 5 Oct 2026
- Optional dependencies
- MDAnalysis and NetworkX are optional; documented utility functions use them for trajectory parsing and coarse-grained graph generation.checked 12 Oct 2026
- Purpose
- HOOMD-TF is a plugin that enables TensorFlow in HOOMD-blue simulations.checked 12 Oct 2026
- Requirements
- The compilation guide lists Python 3.6 or later, TensorFlow 2.3 or later, and HOOMD-blue 2.6.0 or later as requirements.checked 12 Oct 2026
- Support and guidance
- The documentation provides a quickstart, compilation instructions, tutorials, API documentation, and a known-issues section.checked 12 Oct 2026
- Training
- Models can be trained during a simulation using HOOMD-blue forces as labels, with an option to train periodically rather than every step.checked 5 Oct 2026
- Use cases
- It supports online force matching, online machine learning, and collective variable calculations using TensorFlow tensor operations.checked 12 Oct 2026
Money
- License and cost
- The project paper says HOOMD-TF is freely available under the MIT license on GitHub.checked 12 Oct 2026
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