InstaDeepAI
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mace-organics

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JulesLT

heloise-chomet commited on May 29

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Update README.md (#5)

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- Update README.md (a9de64b6ea585c8f4884eb0ec2bf0fc85b77c397)

Co-authored-by: Heloise Chomet <heloise-chomet@users.noreply.huggingface.co>

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README.md +4 -4

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@@ -6,7 +6,7 @@ Mace: Higher order equivariant message passing neural networks for fast and accu
 URL: https://arxiv.org/abs/2206.07697
 ## 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                                                                          |
 |----------------------------------|----------------------|--------------------------------------------------------------------------------------|
@@ -27,7 +27,7 @@ For complete usage instructions and more information, please refer to our [docum
 For more information about MACE hyperparameters,
-please refer to our [documentation](https://instadeep.github.io/mlip/api_reference/models/mace.html#mlip.models.visnet.config.MaceConfig)
 ## Training
 Training is performed over 220 epochs, with an exponential moving average (EMA) decay rate of 0.99.
 The model employs a MSE loss function with scheduled weights for the energy and force components.
@@ -47,7 +47,7 @@ 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                                |
 |-----------------------------|-------|--------------------------------------------|
@@ -59,7 +59,7 @@ 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

 URL: https://arxiv.org/abs/2206.07697
 ## 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 MACE hyperparameters,
+please refer to our [documentation](https://instadeepai.github.io/mlip/api_reference/models/mace.html#mlip.models.visnet.config.MaceConfig)
 ## Training
 Training is performed over 220 epochs, with an exponential moving average (EMA) decay rate of 0.99.
 The model employs a MSE 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