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--- |
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license: apache-2.0 |
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library_name: transformers |
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datasets: |
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- vector-institute/s2ef-15m |
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metrics: |
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- mae |
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pipeline_tag: graph-ml |
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--- |
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# AtomFormer base model |
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This model is a transformer-based model that leverages gaussian pair-wise positional embeddings to train on atomistic graph data. It |
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is part of a suite of datasets/models/utilities in the AtomGen project that supports other methods for pre-training and fine-tuning |
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models on atomistic graphs. |
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## Model description |
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AtomFormer is a transformer model with modifcations to train on atomstic graphs. It builds primarily on the work |
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from uni-mol+ to add the pair-wise pos. embeds. to the attention mask to leverage 3-D positional information. |
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This model was pre-trained on a diverse set of aggregated atomistic datasets where the target task is the per-atom |
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force prediction and the per-system energy prediction. |
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The model also includes metadata regarding the atomic species that are being modeled, this includes the atomic radius, |
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electronegativity, valency, etc. The metadata is normalized and projected to be added to the atom embeddings in the model. |
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## Intended uses & limitations |
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You can use the raw model for either force and energy prediction, but it's mostly intended to |
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be fine-tuned on a downstream task. The performance of the model as a force and energy prediction model |
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is not validated, it was primarily used a pre-training task. |
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### How to use |
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You can use this model directly by loading via the Structure2EnergyandForces task: |
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```python |
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>>> from transformers import AutoModel |
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``` |
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Here is how to use this model to get the features of a given atomistic graph in PyTorch: |
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```python |
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from transformers import AutoModel |
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``` |
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## Training data |
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AtomFormer is trained on an aggregated S2EF dataset from multiple sources such as OC20, OC22, ODAC23, MPtrj, and SPICE |
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with structures and energies/forces for pre-training. The pre-training data includes total energies and formation |
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energies but trains using formation energy (which isn't included for OC22, indicated by "has_formation_energy" column). |
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## Training procedure |
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### Preprocessing |
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The model expects input in the form of tokenized atomic symbols represented as `input_ids` and 3D coordinates represented |
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as `coords`. For the pre-training task it also expects labels for the `forces` and `formation_energy`. |
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The `DataCollatorForAtomModeling` utility in the AtomGen library has the capacity to perform dynamic padding to batch the |
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data together. It also offers the option to flatten the data and provide a `batch` column for gnn-style training. |
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### Pretraining |
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The model was trained on a node of 4xA40 (48 GB) for 10 epochs (~2 weeks). See the |
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[training code](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) for all hyperparameters |
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details. |
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## Evaluation results |
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We use the Atom3D dataset to evaluate the model's performance on downstream tasks. |
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When fine-tuned on downstream tasks, this model achieves the following results: |
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| Task | SMP | PIP | RES | MSP | LBA | LEP | PSR | RSR | |
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|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| |
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| | TBD | TBD | TBD | TBD | TBD | TBD | TBD | TBD | |
