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atomformer-base-smp

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atomformer

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akore commited on Jul 12, 2024

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+license: apache-2.0
+datasets:
+- vector-institute/s2ef-15m
+- vector-institute/atom3d-smp
+metrics:
+- mae
 ---
+# AtomFormer base model Finetuned on Small Molecule Prediction task (SMP)
+This model is a transformer-based model that leverages gaussian pair-wise positional embeddings to train on atomistic graph data.  It
+is part of a suite of datasets/models/utilities in the AtomGen project that supports other methods for pre-training and fine-tuning
+models on atomistic graphs.  This particular model is pre-trained on the `s2ef-15m` dataset and finetuned on the `atom3d-smp` dataset.
+## Model description
+AtomFormer is a transformer model with modifcations to train on atomstic graphs.  It builds primarily on the work
+from uni-mol+ to add the pair-wise pos. embeds. to the attention mask to leverage 3-D positional information.
+This model was pre-trained on a diverse set of aggregated atomistic datasets where the target task is the per-atom
+force prediction and the per-system energy prediction.
+The model also includes metadata regarding the atomic species that are being modeled, this includes the atomic radius,
+electronegativity, valency, etc.  The metadata is normalized and projected to be added to the atom embeddings in the model.
+## Intended uses & limitations
+You can use the model to predict properties for small molecules.
+### How to use
+Here is how to use the model to extract features from the pre-trained backbone:
+```python
+import torch
+from transformers import AutoModelForSequenceClassification
+model = AutoModelForSequenceClassification.from_pretrained("vector-institute/atomformer-base-smp",
+                                   trust_remote_code=True)
+input_ids = torch.randint(0, 50, (1, 10))
+coords = torch.randn(1, 10, 3)
+attn_mask = torch.ones(1, 10)
+output = model(input_ids, coords=coords, attention_mask=attn_mask)
+output[1].shape #  (torch.Size([1, 20])
+```
+## Training data
+AtomFormer is trained on an aggregated S2EF dataset from multiple sources such as OC20, OC22, ODAC23, MPtrj, and SPICE
+with structures and energies/forces for pre-training.  The pre-training data includes total energies and formation
+energies but trains using formation energy (which isn't included for OC22, indicated by "has_formation_energy" column).
+This model variant is finetuned on the small molecule prediction task where it outputs 20 different properties for each sample.
+### Preprocessing
+The model expects input in the form of tokenized atomic symbols represented as `input_ids` and 3D coordinates represented
+as `coords`.  For the pre-training task it also expects labels for the `forces` and `formation_energy`.
+The `DataCollatorForAtomModeling` utility in the AtomGen library has the capacity to perform dynamic padding to batch the
+data together.  It also offers the option to flatten the data and provide a `batch` column for gnn-style training.
+## Evaluation results
+The model is trained for 300 epochs with a batch size of 512, learning rate of 1e-3, cosine decay to zero, max_grad_norm of 5.0, and weight decay of 1e-2.
+Comparison between leveraging the pre-trained base model and training from scratch on the SMP task:
+|  | base | scratch  |
+|:----:|:----:|:----:|:----:|
+|   val   | 0.2304 | 0.1766 |
+|   test   | 1.077 | 1.13 |