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
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.
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.
ViSNet
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.
eSEN
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.
Relevant documentation
For a complete usage instructions, model configs and more information, please refer to our
documentation.
We also present all model hyperparameters and training strategy in the associate
white paper.
These models were trained on the
SPICE2_curated dataset.
For more information about dataset configuration please refer to our
documentationLicense summary
- The Licensed Models are only available under this License for Non-Commercial Purposes.
- 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.
- You may not use the Licensed Models or any of its Outputs in connection with:
- any Commercial Purposes, unless agreed by Us under a separate licence;
- 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;
- 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
- in violation of any applicable laws and regulations.
