Update README.md

README.md

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-# eSEN
-
-## Reference
-
-TBD
-
-## How to Use
-
-For complete usage instructions, please refer to our [documentation](https://instadeepai.github.io/mlip)
-
-## Model architecture
-| Parameter                 | Value                                         | Description                                 |
-|---------------------------|-----------------------------------------------|---------------------------------------------|
-| `num_layers`              | `5`                                           | Number of NequIP layers.                    |
-| `node_irreps`             | `64x0e + 64x0o + 32x1e + 32x1o + 4x2e + 4x2o` | O3 representation space of node features.   |
-| `l_max`                   | `2`                                           | Maximal degree of spherical harmonics.      |
-| `num_bessel`              | `8`                                           | Number of Bessel basis functions.           |
-| `radial_net_nonlinearity` | `swish`                                       | Activation function for radial MLP.         |
-| `radial_net_n_hidden`     | `64`                                          | Number of hidden features in radial MLP.    |
-| `radial_net_n_layers`     | `2`                                           | Number of layers in radial MLP.             |
-| `radial_envelope`         | `polynomial_envelope`                         | Radial envelope function.                   |
-| `scalar_mlp_std`          | `4`                                           | Standard deviation of weight initialisation.|
-| `atomic_energies`         | `None`                                        | Treatment of the atomic energies.           |
-| `avg_um_neighbors`        | `None`                                        | Mean number of neighbors.                   |
-
-For more information about NequIP hyperparameters, 
-please refer to our [documentation](https://instadeepai.github.io/mlip/api_reference/models/nequip.html#mlip.models.nequip.config.NequipConfig)
-
-## Training
-
-Training is performed over 220 epochs, with an exponential moving average (EMA) decay rate of 0.99.
-The model employs a Huber loss function with scheduled weights for the energy and force components. 
-Initially, the energy term is weighted at 40 and the force term at 1000.
-At epoch 115, these weights are flipped.
-
-We use our default MLIP optimizer in v1.0.0 with the following settings:
-| Parameter                        | Value          | Description                                                     |
-|----------------------------------|----------------|-----------------------------------------------------------------|
-| `init_learning_rate`             | `0.002`        | Initial learning rate.                                          |
-| `peak_learning_rate`             | `0.002`        | Peak learning rate.                                             |
-| `final_learning_rate`            | `0.002`        | Final learning rate.                                            |
-| `weight_decay`                   | `0`            | Weight decay.                                                   |
-| `warmup_steps`                   | `4000`         | Number of optimizer warm-up steps.                              |
-| `transition_steps`               | `360000`       | Number of optimizer transition steps.                           |
-| `grad_norm`                      | `500`          | Gradient norm used for gradient clipping.                       |
-| `num_gradient_accumulation_steps`| `1`            | Steps to accumulate before taking an optimizer step.            |
-
-For more information about the optimizer, 
-please refer to our [documentation](https://instadeepai.github.io/mlip/api_reference/training/optimizer.html#mlip.training.optimizer_config.OptimizerConfig)
-## Dataset
-| Parameter                   | Value | Description                                |
-|-----------------------------|-------|--------------------------------------------|
-| `graph_cutoff_angstrom`     | `5`   | Graph cutoff distance (in Å).              |
-| `max_n_node`                | `32`  | Maximum number of nodes allowed in a batch.|
-| `max_n_edge`                | `288` | Maximum number of edges allowed in a batch.|
-| `batch_size`                | `16`  | Number of graphs in a batch.               |
-
-This model was trained on the [SPICE2_curated dataset](https://huggingface.co/datasets/InstaDeepAI/SPICE2-curated).
-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
+# 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. 
+
+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
+
+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
+
+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.