docs: add finetune docs (microsoft#78)

Co-authored-by: Xixian <[email protected]>

README.md

@@ -119,6 +119,19 @@ To switch to the `5M` version, manually set the `load_path` of `MatterSimCalcula
 MatterSimCalculator(load_path="MatterSim-v1.0.0-5M.pth", device=device)
 ```
 
+## Finetune
+> [!TIP]
+> MatterSim provides a finetune script to finetune the pre-trained MatterSim model on a custom dataset.
+> Please refer to the [MatterSim documentation](https://microsoft.github.io/mattersim/) for more details.
+
+### A minimal finetune example
+
+```bash
+torchrun --nproc_per_node=1 src/mattersim/training/finetune_mattersim.py --load_model_path mattersim-v1.0.0-1m --train_data_path tests/data/high_level_water.xyz
+```
+
+
+
 
 ## Reference
 

docs/index.rst

@@ -82,4 +82,5 @@ Frequently Asked Questions
 
    user_guide/installation
    user_guide/getting_started
+   user_guide/finetune
    examples/examples

docs/user_guide/finetune.rst

@@ -0,0 +1,86 @@
+
+Finetune MatterSim
+==================
+
+Finetune Script
+---------------
+
+MatterSim provides a finetune script to
+finetune the pre-trained MatterSim model on a custom dataset.
+You can find the script in the ``training`` folder or in the
+`github link <https://github.com/microsoft/mattersim/blob/main/src/mattersim/training/finetune_mattersim.py>`_.
+
+Finetune Parameters
+--------------------
+
+The finetune script accepts several command-line arguments to customize the training process. Below is a list of the available parameters:
+
+- **run_name**: (str) The name of the run. Default is "example".
+
+- **train_data_path**: (str) Path to the training data file. Supports various file types readable by ASE (e.g., `.xyz`, `.traj`, `.cif`) and `.pkl` files. Default is "./sample.xyz".
+
+- **valid_data_path**: (str) Path to the validation data file. Default is None.
+
+- **load_model_path**: (str) Path to load the pre-trained model. Default is "mattersim-v1.0.0-1m".
+
+- **save_path**: (str) Path to save the trained model. Default is "./results".
+
+- **save_checkpoint**: (bool) Whether to save checkpoints during training. Default is False.
+
+- **ckpt_interval**: (int) Interval (in epochs) to save checkpoints. Default is 10.
+
+- **device**: (str) Device to use for training, either "cuda" or "cpu". Default is "cuda".
+
+- **cutoff**: (float) Cutoff radius for interactions. Default is 5.0.
+
+- **threebody_cutoff**: (float) Cutoff radius for three-body interactions, should be smaller than the two-body cutoff. Default is 4.0.
+
+- **epochs**: (int) Number of training epochs. Default is 1000.
+
+- **batch_size**: (int) Batch size for training. Default is 16.
+
+- **lr**: (float) Learning rate for the optimizer. Default is 2e-4.
+
+- **step_size**: (int) Step size for the learning rate scheduler. Default is 10.
+
+- **include_forces**: (bool) Whether to include forces in the training. Default is True.
+
+- **include_stresses**: (bool) Whether to include stresses in the training. Default is False.
+
+- **force_loss_ratio**: (float) Ratio of force loss in the total loss. Default is 1.0.
+
+- **stress_loss_ratio**: (float) Ratio of stress loss in the total loss. Default is 0.1.
+
+- **early_stop_patience**: (int) Patience for early stopping. Default is 10.
+
+- **seed**: (int) Random seed for reproducibility. Default is 42.
+
+- **re_normalize**: (bool) Whether to re-normalize energy and forces according to new data. Default is False.
+
+- **scale_key**: (str) Key for scaling forces. Only used when ``re_normalize`` is True. Default is "per_species_forces_rms".
+
+- **shift_key**: (str) Key for shifting energy. Only used when ``re_normalize`` is True. Default is "per_species_energy_mean_linear_reg".
+
+- **init_scale**: (float) Initial scale value. Only used when ``re_normalize`` is True. Default is None.
+
+- **init_shift**: (float) Initial shift value. Only used when ``re_normalize`` is True. Default is None.
+
+- **trainable_scale**: (bool) Whether the scale is trainable. Only used when ``re_normalize`` is True. Default is False.
+
+- **trainable_shift**: (bool) Whether the shift is trainable. Only used when ``re_normalize`` is True. Default is False.
+
+- **wandb**: (bool) Whether to use Weights & Biases for logging. Default is False.
+
+- **wandb_api_key**: (str) API key for Weights & Biases. Default is None.
+
+- **wandb_project**: (str) Project name for Weights & Biases. Default is "wandb_test".
+
+These parameters allow you to customize the finetuning process to suit your specific dataset and computational resources.
+
+Finetune Example
+----------------
+You can replace the data path with your own data path.
+
+.. code-block:: bash
+
+    torchrun --nproc_per_node=1 src/mattersim/training/finetune_mattersim.py --load_model_path mattersim-v1.0.0-1m --train_data_path xyz_files/train.xyz --valid_data_path xyz_files/valid.xyz --batch_size 16 --lr 2e-4 --step_size 20 --epochs 200 --save_path ./finetune_result --save_checkpoint --ckpt_interval 20 --include_stresses --include_forces