Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,32 @@
+---
+license: other
+license_name: mixed
+license_link: LICENSE.md
+library_name: safetensors
+tags: [interatomic-potential, chemistry, materials, webgpu]
+---
+
+# Models for MLIP-Visualization
+
+Converted weights for [MLIP-Visualization](https://github.com/EricBoittier/MLIP-Visualization),
+which runs machine-learning interatomic potentials in the browser and draws every forward and
+backward pass. The app downloads these files directly; they are plain safetensors plus a JSON
+file of hyperparameters per model.
+
+| file | model | source | licence |
+|---|---|---|---|
+| `pet-mad-xs` | PET-MAD XS | lab-cosmo/upet, converted with convert_pet.py | BSD-3-Clause |
+| `ani-2x` | ANI-2x | TorchANI models.ANI2x(), converted with convert_ani.py | MIT |
+| `physnet-acetone` | PhysNet · acetone MP2 | mmml physnetjax, trained with train_physnet_demo.py | MIT |
+| `pet-mols-s-v1.0` | PET-MOLS S v1.0 | lab-cosmo/upet, converted with convert_pet.py | BSD-3-Clause |
+
+`index.json` lists what the app offers.
+
+## Credits
+
+- **PET-MAD / PET-MOLS**: [lab-cosmo/upet](https://huggingface.co/lab-cosmo/upet) (BSD-3-Clause), converted with
+  [pet-kokkos](https://github.com/EricBoittier/pet-kokkos)'s `convert_pet.py`. Cite the PET and PET-MAD papers when you use them.
+- **ANI-2x**: [TorchANI](https://github.com/aiqm/torchani) (MIT), Devereux et al., *J. Chem. Theory Comput.* 16, 4192 (2020),
+  converted with `scripts/convert_ani.py`.
+- **PhysNet (acetone)**: a small invariant PhysNet ([mmml](https://github.com/EricBoittier/mmml) `physnetjax`, MIT,
+  max_degree = 0) trained for this app on mmml's acetone-dimer MP2 example data, with `scripts/train_physnet_demo.py`.