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@@ -232,7 +232,7 @@ mattergen-finetune adapter.pretrained_name=$MODEL_NAME data_module=mp_20 +lightn | |
| #### Fine-tune on your own property data | ||
| You may also fine-tune MatterGen on your own property data. Essentially what you need is a property value (typically `float`) for a subset of the data you want to train on (e.g., `alex_mp_20`). Proceed as follows: | ||
| 1. Add the name of your property to the `PROPERTY_SOURCE_IDS` list inside [`mattergen/mattergen/common/utils/globals.py`](mattergen/mattergen/common/utils/globals.py). | ||
| 1. Add the name of your property to the `PROPERTY_SOURCE_IDS` list inside [`mattergen/common/utils/globals.py`](mattergen/common/utils/globals.py). | ||
| 2. Add a new column with this name to the dataset(s) you want to train on, e.g., `datasets/alex_mp_20/train.csv` and `datasets/alex_mp_20/val.csv` (requires you to have followed the [pre-processing steps](#pre-process-a-dataset-for-training)). | ||
| 3. Re-run the CSV to dataset script `csv-to-dataset --csv-folder datasets/<MY_DATASET>/ --dataset-name <MY_DATASET> --cache-folder datasets/cache`, substituting your dataset name for `MY_DATASET`. | ||
| 4. Add a `<your_property>.yaml` config file to [`mattergen/conf/lightning_module/diffusion_module/model/property_embeddings`](mattergen/conf/lightning_module/diffusion_module/model/property_embeddings). If you are adding a float-valued property, you may copy an existing configuration, e.g., [`dft_mag_density.yaml`](mattergen/conf/lightning_module/diffusion_module/model/property_embeddings/dft_mag_density.yaml). More complicated properties will require you to create your own custom `PropertyEmbedding` subclass, e.g., see the [`space_group`](mattergen/conf/lightning_module/diffusion_module/model/property_embeddings/space_group.yaml) or [`chemical_system`](mattergen/conf/lightning_module/diffusion_module/model/property_embeddings/chemical_system.yaml) configs. | ||
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@@ -285,4 +285,4 @@ If you have any questions not covered here, please create an issue or contact th | |
| Materials Design team at [[email protected]](mailto:[email protected]). | ||
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| We would appreciate your feedback and would like to know how MatterGen has been beneficial to your research. | ||
| Please share your experiences with us at [[email protected]](mailto:[email protected]). | ||
| Please share your experiences with us at [[email protected]](mailto:[email protected]). | ||