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| license: apache-2.0 |
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| |
| # OrbMol: Extending Orb to Molecular Systems |
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| Built on the [Orb-v3 architecture](https://arxiv.org/abs/2504.06231), OrbMol is a universal interatomic potential trained on the [Open Molecules 2025 (OMol25)](https://arxiv.org/pdf/2505.08762) dataset—over 100M high-accuracy DFT calculations (ωB97M-V/def2-TZVPD). |
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| Unlike its predecessors, OrbMol accepts total charge and spin multiplicity, enabling it to accurately model open-shell, ionic electrolytes, metal complexes and biomolecules. |
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| ## Model checkpoints |
| There are two model checkpoints available: |
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| - **OrbMol**: conservative default model |
| - **OrbMol-direct**: direct variant (i.e. forces are not the exact gradient of the energy) |
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| Both models use a 6 Å radius cutoff with "infinite" (120) max neighbours in the neighborlist computation. |
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| | Name | Checkpoint File | Hash Prefix | |
| |---------------|------------------------------------------------------|-------------| |
| | **OrbMol** | `orb-v3-conservative-omol-20250820.safetensors` | dc9964d66d54 | |
| | **OrbMol-direct** | `orb-v3-direct-omol-20250820.safetensors` | — | |
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| ## 🚀 Quick Start |
| Head to the [orb-models](https://github.com/orbital-materials/orb-models) Github repository for complete instructions. In a nutshell: |
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| ```bash |
| pip install orb-models |
| pip install --extra-index-url=https://pypi.nvidia.com "cuml-cu11==25.2.*" # For cuda versions >=11.4, <11.8 |
| pip install --extra-index-url=https://pypi.nvidia.com "cuml-cu12==25.2.*" # For cuda versions >=12.0, <13.0 |
| ``` |
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| ```python |
| import ase |
| from ase.build import molecule |
| from orb_models.forcefield import atomic_system, pretrained |
| from orb_models.forcefield.base import batch_graphs |
| |
| device = "cpu" # or device="cuda" |
| orbff = pretrained.orb_v3_conservative_omol( |
| device=device, |
| precision="float32-high", # or "float32-highest" / "float64 |
| ) |
| atoms = molecule("C6H6") |
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| atoms.info["charge"] = 0 # total charge |
| atoms.info["spin"] = 1 # spin multiplicity |
| graph = atomic_system.ase_atoms_to_atom_graphs(atoms, orbff.system_config, device=device) |
| |
| result = orbff.predict(graph, split=False) |
| ``` |
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| ## Support |
| If you run into any issues feel free to post your questions or comments on our [Github Issues page](https://github.com/orbital-materials/orb-models/issues). |
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| ## License |
| ORB models are licensed under the Apache License, Version 2.0. Please see the [LICENSE file](https://github.com/orbital-materials/orb-models/blob/main/LICENSE) for details. |
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| --- |
| ## Citation |
| If you use this work, please cite: |
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| ``` |
| @misc{rhodes2025orbv3atomisticsimulationscale, |
| title={Orb-v3: atomistic simulation at scale}, |
| author={Benjamin Rhodes and Sander Vandenhaute and Vaidotas Šimkus and James Gin and Jonathan Godwin and Tim Duignan and Mark Neumann}, |
| year={2025}, |
| eprint={2504.06231}, |
| archivePrefix={arXiv}, |
| primaryClass={cond-mat.mtrl-sci}, |
| url={https://arxiv.org/abs/2504.06231}, |
| } |
| |
| @misc{neumann2024orbfastscalableneural, |
| title={Orb: A Fast, Scalable Neural Network Potential}, |
| author={Mark Neumann and James Gin and Benjamin Rhodes and Steven Bennett and Zhiyi Li and Hitarth Choubisa and Arthur Hussey and Jonathan Godwin}, |
| year={2024}, |
| eprint={2410.22570}, |
| archivePrefix={arXiv}, |
| primaryClass={cond-mat.mtrl-sci}, |
| url={https://arxiv.org/abs/2410.22570}, |
| } |
| ``` |
