Update README.md
Browse filesUpdated model card with links to documentation
README.md
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## License summary
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1. The Licensed Models are **only** available under this License for Non-Commercial Purposes.
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# ViSNet
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## Reference
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Yusong Wang, Tong Wang, Shaoning Li, Xinheng He, Mingyu Li, Zun Wang, Nanning Zheng, Bin Shao, and Tie-Yan Liu.
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Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing.
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Nature Communications, 15(1), January 2024. ISSN: 2041-1723.
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URL: https://dx.doi.org/10.1038/s41467-023-43720-2.
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## How to Use
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For complete usage instructions and more information, please refer to our [documentation](https://instadeep.github.io/mlip)
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## Model architecture
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| Parameter | Value | Description |
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|--------------------|----------|--------------------------------------------------------------------------|
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| `num_layers` | `4` | Number of ViSNet layers. |
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| `num_channels` | `128` | Number of channels. |
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| `l_max` | `2` | Highest harmonic order included in the Spherical Harmonics series. |
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| `num_heads` | `8` | Number of heads in the attention block. |
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| `num_rbf` | `32` | Number of radial basis functions in the embedding block. |
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| `trainable_rbf` | `False` | Whether to add learnable weights to the radial embedding basis functions.|
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| `activation` | `silu` | Activation function for the output block. |
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| `attn_activation` | `silu` | Activation function for the attention block. |
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| `vecnorm_type` | `None` | Type of the vector norm. |
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| `atomic_energies` | `average`| Treatment of the atomic energies. |
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| `avg_um_neighbors` | `None` | Mean number of neighbors. |
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For more information about ViSNet hyperparameters,
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please refer to our [documentation](https://instadeep.github.io/mlip/api_reference/models/visnet.html#mlip.models.visnet.config.VisnetConfig)
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## Training
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Training is performed over 220 epochs, with an exponential moving average (EMA) decay rate of 0.99.
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The model employs a Huber loss function with scheduled weights for the energy and force components.
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Initially, the energy term is weighted at 40 and the force term at 1000.
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At epoch 115, these weights are flipped.
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We use our default MLIP optimizer in v1.0.0 with the following settings:
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| Parameter | Value | Description |
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|----------------------------------|----------------|-----------------------------------------------------------------|
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| `init_learning_rate` | `0.0001` | Initial learning rate. |
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| `peak_learning_rate` | `0.0001` | Peak learning rate. |
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| `final_learning_rate` | `0.0001` | Final learning rate. |
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| `weight_decay` | `0` | Weight decay. |
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| `warmup_steps` | `4000` | Number of optimizer warm-up steps. |
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| `transition_steps` | `360000` | Number of optimizer transition steps. |
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| `grad_norm` | `500` | Gradient norm used for gradient clipping. |
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| `num_gradient_accumulation_steps`| `1` | Steps to accumulate before taking an optimizer step. |
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For more information about the optimizer,
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please refer to our [documentation](https://instadeep.github.io/mlip/api_reference/training/optimizer.html#mlip.training.optimizer_config.OptimizerConfig)
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## Dataset
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| Parameter | Value | Description |
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|-----------------------------|-------|--------------------------------------------|
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| `graph_cutoff_angstrom` | `5` | Graph cutoff distance (in Å). |
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| `max_n_node` | `32` | Maximum number of nodes allowed in a batch.|
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| `max_n_edge` | `288` | Maximum number of edges allowed in a batch.|
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| `batch_size` | `16` | Number of graphs in a batch. |
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This model was trained on the [SPICE2_curated dataset](https://huggingface.co/datasets/InstaDeepAI/SPICE2-curated).
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For more information about dataset configuration
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please refer to our [documentation](https://instadeep.github.io/mlip/api_reference/data/dataset_configs.html#mlip.data.configs.GraphDatasetBuilderConfig)
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## License summary
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1. The Licensed Models are **only** available under this License for Non-Commercial Purposes.
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