Scikit-learn
Joblib
chemistry
computational-chemistry
catalysis
random-forest
graph-neural-network
regression
Instructions to use cazorazitora/benchmark-dg-ddg-for-garden with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use cazorazitora/benchmark-dg-ddg-for-garden with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("cazorazitora/benchmark-dg-ddg-for-garden", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Ni-catalysed alkyne carboxylation: ΔG and ΔΔG models
Trained weights and the descriptor pipeline for three models that predict the reaction free energy (ΔG) and the regioselectivity gap (ΔΔG) of Ni-catalysed alkyne carboxylation with CO₂ over bipyridine ligands.
This repository exists to serve the artifacts to a
Garden deployment: Garden rebuilds its container from a
single app file, so the weights and the descriptor code have to be fetchable from
a public URL at image-build time. Everything here is downloaded by
wget during that build.
Contents
| File | Size | What it is |
|---|---|---|
egnn_best.pt |
48 MB | EGNN checkpoint (weights + target normalisation + hyperparameters) |
rf_dg.joblib |
322 MB | RandomForestRegressor for ΔG, 800 trees |
rf_ddg.joblib |
145 MB | RandomForestRegressor for ΔΔG, 800 trees |
rf_src.tar.gz |
39 KB | The descriptor pipeline (geometry → 118 descriptors) |
PROVENANCE.txt |
— | Source commit and per-file transformation record |
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