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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 |
| `mace-mp-0b3-medium` | MACE-MP-0b3 medium | mace-foundations/mace-mp-0, converted with convert_mace.py | MIT |
| `mace-mp-0b2-small` | MACE-MP-0b2 small | mace-foundations/mace-mp-0, converted with convert_mace.py | MIT |
| `lorem-demo` | LOREM demo | metatrain experimental LOREM, random initialization from convert_lorem.py | MIT |
| `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.
- **MACE-MP-0**: [mace-foundations/mace-mp-0](https://huggingface.co/mace-foundations/mace-mp-0) (MIT), Batatia et al.,
*A foundation model for atomistic materials chemistry* (2023), converted with `scripts/convert_mace.py`.
- **LOREM demo**: a random initialization of [metatrain](https://github.com/lab-cosmo/metatrain)'s experimental LOREM
(Bigi et al., arXiv:2507.19382), exported with `scripts/convert_lorem.py`. Not a trained potential.
- **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`.
|