rearrange readme

.github/README.md

@@ -19,31 +19,44 @@ MLIP Arena is now in pre-alpha. If you're interested in joining the effort, plea
 
 If you have pretrained MLIP models that you would like to contribute to the MLIP Arena and show benchmark in real-time, there are two ways:
 
-#### Hugging Face Model
+#### Hugging Face Model (recommended, difficult)
 
 0. Inherit Hugging Face [ModelHubMixin](https://huggingface.co/docs/huggingface_hub/en/package_reference/mixins) class to your awesome model class definition. We recommend [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/en/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin).
 1. Create a new [Hugging Face Model](https://huggingface.co/new) repository and upload the model file using [push_to_hub function](https://huggingface.co/docs/huggingface_hub/en/package_reference/mixins#huggingface_hub.ModelHubMixin.push_to_hub).
-2. Follow the template to code the I/O interface for your model, and upload the script along with metadata to the MLIP Arena [here](../mlip_arena/models/README.md).
-3. CPU benchmarking will be performed automatically. Due to the limited amount GPU compute, if you would like to be considered for GPU benchmarking, please create a pull request to demonstrate the offline performance of your model (published paper or preprint). We will review and select the models to be benchmarked on GPU.
+2. Follow the template to code the I/O interface for your model [here](../mlip_arena/models/README.md). 
+3. Update model [registry](../mlip_arena/models/registry.yaml) with metadata
+
+> [!NOTE] CPU benchmarking will be performed automatically. Due to the limited amount GPU compute, if you would like to be considered for GPU benchmarking, please create a pull request to demonstrate the offline performance of your model (published paper or preprint). We will review and select the models to be benchmarked on GPU.
+
+#### External ASE Calculator (easy)
+
+1. Implement new ASE Calculator class in [mlip_arena/models/external.py](../mlip_arena/models/externals.py). 
+2. Name your class with awesome model name and add the same name to [registry](../mlip_arena/models/registry.yaml) with metadata.
+
+> [!CAUTION] Remove unneccessary outputs under `results` class attributes to avoid error for MD simulations. Please refer to other class definition for example.
 
 ### Add new benchmark tasks
 
-1. Create a new [Hugging Face Dataset](https://huggingface.co/new-dataset) repository and upload the reference data (e.g. DFT, AIMD, experimental measurements such as RDF).
-2. Follow the task template to implement the task class and upload the script along with metadata to the MLIP Arena [here](../mlip_arena/tasks/README.md).
-3. Code a benchmark script to evaluate the performance of your model on the task. The script should be able to load the model and the dataset, and output the evaluation metrics.
+1. Follow the task template to implement the task class and upload the script along with metadata to the MLIP Arena [here](../mlip_arena/tasks/README.md).
+2. Code a benchmark script to evaluate the performance of your model on the task. The script should be able to load the model and the dataset, and output the evaluation metrics.
 
-#### Molecular dynamics calculations
+### Add new datasets
 
-- [ ] [MD17](http://www.sgdml.org/#datasets)
-- [ ] [MD22](http://www.sgdml.org/#datasets)
+1. Create a new [Hugging Face Dataset](https://huggingface.co/new-dataset) repository and upload the reference data (e.g. DFT, AIMD, experimental measurements such as RDF).
 
 
 #### Single-point density functional theory calculations
 
 - [ ] MPTrj
+- [ ] [Alexandria](https://huggingface.co/datasets/atomind/alexandria)
 - [ ] QM9
-- [ ] [Alexandria](https://alexandria.icams.rub.de/)
 
-### Add new training datasets
+#### Molecular dynamics calculations
+
+- [ ] [MD17](http://www.sgdml.org/#datasets)
+- [ ] [MD22](http://www.sgdml.org/#datasets)
+
+
+### [Hugging Face Auto-Train](https://huggingface.co/docs/hub/webhooks-guide-auto-retrain)
 
-[Hugging Face Auto-Train](https://huggingface.co/docs/hub/webhooks-guide-auto-retrain)
+Planned but not yet impelemented.