GraphChem: Graph-based machine learning for chemical property prediction
GraphChem is an open source Python package for constructing graph-based machine learning models with a focus on fuel property prediction.
Installation
Prerequisites
- Python 3.10 or newer
Method 1: pip
$ pip install graphchem
Method 2: From source
$ git clone https://github.com/ecrl/graphchem
$ cd graphchem
$ python -m pip install .
If installation fails for RDKit, PyTorch, or torch-geometric, follow the vendor guides: RDKit, PyTorch, PyTorch Geometric.
Documentation
- Read the Docs (Sphinx) — installation, quickstart, API, datasets, stability policy
- API_STABILITY.md — source-tree pointer to the frozen public surface
- CHANGELOG.md
Examples
Notebooks are under examples/ on GitHub. See also the tutorials page in the docs.
For reproducible runs, set seeds before training or evaluation, for example:
import numpy as np
import torch
torch.manual_seed(0)
np.random.seed(0)
Exact numeric results can still vary across platforms and dependency wheel builds (RDKit / PyTorch / PyG).
Contributing and support
- Contribute via pull request; follow CONTRIBUTING.md
- Report bugs and feature requests with GitHub Issues
- Security reports: SECURITY.md
- Code of conduct: CODE_OF_CONDUCT.md
For additional questions, contact Travis Kessler (travis.j.kessler@gmail.com).
1 maintainer
Metadata
1 maintainer
Release files for graphchem 2.3.4
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| File | Size | Uploaded | Provenance | |
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|---|---|---|---|---|
| graphchem-2.3.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 56.6 kB
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