Atom-JEPA

Embeddings from the pretrained encoders of Atom-JEPA: Joint-Embedding Predictive Architecture for 3D Atomistic Systems.

Atom-JEPA is a self-supervised pretraining method for 3D atomistic systems. It learns by predicting the latent representations of one part of a structure from another, and the same recipe applies to both molecules and crystals. This package loads the pretrained encoders and turns 3D structures into embeddings. Pretraining and fine-tuning code is in the GitHub repository.

Installation

Install PyTorch first (pytorch.org), then:

pip install atom-jepa            # structures as arrays or ase.Atoms
pip install "atom-jepa[rdkit]"   # + SMILES input
pip install "atom-jepa[all]"     # + SMILES and pymatgen input

Usage

from atom_jepa import AtomJEPA

model = AtomJEPA.from_pretrained("molecules")    # or "crystals"

emb = model.embed("CC(=O)Oc1ccccc1C(=O)O")       # one structure -> [256]
embs = model.embed(["CCO", "c1ccncc1"])          # a list -> [2, 256]

The weights (molecules, crystals) are downloaded from huggingface.co/atom-jepa/atom-jepa on first use and cached locally. A path to a local checkpoint also works.

embed accepts any of these as one structure, or a list of them:

For several conformers per molecule, use atom_jepa.conformers.smiles_to_samples(smiles, num_conformers=10) and embed the list.

Feature options

model.embed(smiles, layers="all")                    # [S, 8, 256]     output of each of the 8 blocks
model.embed(smiles, layers=[4, 8])                   # [S, 2, 256]
model.embed(smiles, degrees="all")                   # [S, 9, 256]     l=0, l=1 (3), l=2 (5) components
model.embed(smiles, degrees="all", invariant=True)   # [S, 3, 256]     l=0 and per-channel norms of l=1, l=2
model.embed(smiles, layers="all", degrees="all")     # [S, 8, 9, 256]
model.embed(smiles, per_atom=True)                   # list of [n_atoms, 256]

• layers="last" (default) is the last block after the encoder's final norm; block numbers 1-8 and "all" are the block outputs before it.
• Structure features are the mean over atoms. The l>0 components are equivariant: l=1 rotates like an (x, y, z) vector. invariant=True makes them rotation invariant.

Fine-tuning

AtomJEPA is a torch.nn.Module: calling it on a batch returns differentiable features (with the same options), so you can train your own head and fine-tune the encoder:

import torch
from torch.utils.data import DataLoader
from atom_jepa import AtomJEPA, to_sample

model = AtomJEPA.from_pretrained("molecules").train()
head = torch.nn.Linear(model.embedding_dim, 1).to(model.device)

data = [{**to_sample(s), "y": torch.tensor([y])} for s, y in zip(train_smiles, train_y)]
loader = DataLoader(data, batch_size=32, shuffle=True, collate_fn=model.collate)
opt = torch.optim.AdamW([{"params": model.parameters(), "lr": 1e-5},
                         {"params": head.parameters(), "lr": 1e-3}])
for batch in loader:
    loss = torch.nn.functional.mse_loss(head(model(batch)), batch["y"].to(model.device))
    opt.zero_grad(); loss.backward(); opt.step()

model.set_grad_checkpointing(True) trades compute for memory on large structures.

License

The code is released under the MIT License. The pretrained models are released under CC BY 4.0.