| # Overview |
|
|
| We present for pre-trained models to illustrate the use of the *mlip* library. Below are the references for each of these models (MACE, NequIP, ViSNet, eSEN). |
| # References |
| ## MACE |
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|
| Ilyes Batatia, Dávid Péter Kovács, Gregor N. C. Simm, Christoph Ortner, and Gábor Csányi. |
| Mace: Higher order equivariant message passing neural networks for fast and accurate force fields, |
| 2023. |
|
|
| URL: https://arxiv.org/abs/2206.07697 |
|
|
| ## NequIP |
|
|
| Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, |
| Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, and Boris Kozinsky. |
| E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. |
| Nature Communications, 13(1), May 2022. ISSN: 2041-1723. |
|
|
| URL: https://dx.doi.org/10.1038/s41467-022-29939-5. |
|
|
| ## ViSNet |
|
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| Yusong Wang, Tong Wang, Shaoning Li, Xinheng He, Mingyu Li, Zun Wang, Nanning Zheng, Bin Shao, and Tie-Yan Liu. |
| Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing. |
| Nature Communications, 15(1), January 2024. ISSN: 2041-1723. |
|
|
| URL: https://dx.doi.org/10.1038/s41467-023-43720-2. |
|
|
| ## eSEN |
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|
| Xiang Fu, Brandon M. Wood, Luis Barroso-Luque, Daniel S. Levine, Meng Gao, Misko |
| Dzamba, and C. Lawrence Zitnick. Learning smooth and expressive interatomic potentials for |
| physical property prediction, 2025. |
|
|
| URL https://arxiv.org/abs/2502.12147. |
|
|
| # Relevant documentation |
|
|
| For a complete usage instructions, model configs and more information, please refer to our [documentation](https://instadeepai.github.io/mlip). |
| We also present all model hyperparameters and training strategy in the associate [white paper](link.TBD). |
|
|
| These models were trained on the [SPICE2_curated dataset](https://huggingface.co/datasets/InstaDeepAI/SPICE2_curated_v2). |
| For more information about dataset configuration please refer to our [documentation](https://instadeepai.github.io/mlip/api_reference/data/dataset_configs.html#mlip.data.configs.GraphDatasetBuilderConfig) |
|
|
| # License summary |
|
|
| 1. The Licensed Models are **only** available under this License for Non-Commercial Purposes. |
| 2. You are permitted to reproduce, publish, share and adapt the Output generated by the Licensed Model only for Non-Commercial Purposes and in accordance with this License. |
| 3. You may **not** use the Licensed Models or any of its Outputs in connection with: |
| 1. any Commercial Purposes, unless agreed by Us under a separate licence; |
| 2. to train, improve or otherwise influence the functionality or performance of any other third-party derivative model that is commercial or intended for a Commercial Purpose and is similar to the Licensed Models; |
| 3. to create models distilled or derived from the Outputs of the Licensed Models, unless such models are for Non-Commercial Purposes and open-sourced under the same license as the Licensed Models; or |
| 4. in violation of any applicable laws and regulations. |
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