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README.md

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-title: MLIP Arena
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 # mlip-arena
 
 MLIP Arena is an open-source platform for benchmarking machine learning interatomic potentials (MLIPs). The platform provides a unified interface for users to evaluate the performance of their models on a variety of tasks, including single-point density functional theory calculations and molecular dynamics simulations. The platform is designed to be extensible, allowing users to contribute new models, benchmarks, and training data to the platform.

index.md

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-# mlip-arena
-
-MLIP Arena is an open-source platform for benchmarking machine learning interatomic potentials (MLIPs). The platform provides a unified interface for users to evaluate the performance of their models on a variety of tasks, including single-point density functional theory calculations and molecular dynamics simulations. The platform is designed to be extensible, allowing users to contribute new models, benchmarks, and training data to the platform.
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-## Contribute
-
-### Add new MLIP models
-
-If you have pretrained MLIP models that you would like to contribute to the MLIP Arena and show benchmark in real-time, please follow these steps:
-
-1. Create a new [Hugging Face Model](https://huggingface.co/new) repository and upload the model file.
-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]().
-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.
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-### Add new benchmark tasks
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-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]().
-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.
-
-#### Molecular dynamics calculations
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-- [ ] [MD17](http://www.sgdml.org/#datasets)
-- [ ] [MD22](http://www.sgdml.org/#datasets)
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-
-#### Single-point density functional theory calculations
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-- [ ] MPTrj
-- [ ] QM9
-- [ ] [Alexandria](https://alexandria.icams.rub.de/)
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-### Add new training datasets
-
-[Hugging Face Auto-Train](https://huggingface.co/docs/hub/webhooks-guide-auto-retrain)