InstaDeepAI
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visnet-organics

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@@ -5,7 +5,7 @@ Enhancing geometric representations for molecules with equivariant vector-scalar
 Nature Communications, 15(1), January 2024. ISSN: 2041-1723.
 URL: https://dx.doi.org/10.1038/s41467-023-43720-2.
 ## How to Use
-For complete usage instructions and more information, please refer to our [documentation](https://instadeep.github.io/mlip)
 ## Model architecture
 | Parameter          | Value    | Description                                                              |
 |--------------------|----------|--------------------------------------------------------------------------|
@@ -23,7 +23,7 @@ For complete usage instructions and more information, please refer to our [docum
 For more information about ViSNet hyperparameters,
-please refer to our [documentation](https://instadeep.github.io/mlip/api_reference/models/visnet.html#mlip.models.visnet.config.VisnetConfig)
 ## 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.
@@ -43,7 +43,7 @@ We use our default MLIP optimizer in v1.0.0 with the following settings:
 For more information about the optimizer,
-please refer to our [documentation](https://instadeep.github.io/mlip/api_reference/training/optimizer.html#mlip.training.optimizer_config.OptimizerConfig)
 ## Dataset
 | Parameter                   | Value | Description                                |
 |-----------------------------|-------|--------------------------------------------|
@@ -55,7 +55,7 @@ please refer to our [documentation](https://instadeep.github.io/mlip/api_referen
 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://instadeep.github.io/mlip/api_reference/data/dataset_configs.html#mlip.data.configs.GraphDatasetBuilderConfig)
 ## License summary

 Nature Communications, 15(1), January 2024. ISSN: 2041-1723.
 URL: https://dx.doi.org/10.1038/s41467-023-43720-2.
 ## How to Use
+For complete usage instructions and more information, please refer to our [documentation](https://instadeepai.github.io/mlip)
 ## Model architecture
 | Parameter          | Value    | Description                                                              |
 |--------------------|----------|--------------------------------------------------------------------------|
 For more information about ViSNet hyperparameters,
+please refer to our [documentation](https://instadeepai.github.io/mlip/api_reference/models/visnet.html#mlip.models.visnet.config.VisnetConfig)
 ## 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.
 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                                |
 |-----------------------------|-------|--------------------------------------------|
 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