AtomGen provides a robust framework for handling atomistic graph datasets focusing on transformer-based implementations. We provide utilities for training various models, experimenting with different pre-training tasks, and pre-trained models.
It streamlines the process of aggregation, standardization, and utilization of datasets from diverse sources, enabling large-scale pre-training and generative modeling on atomistic graphs.
AtomGen facilitates the aggregation and standardization of datasets, including but not limited to:
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S2EF Datasets: Aggregated from multiple sources such as OC20, OC22, ODAC23, MPtrj, and SPICE with structures and energies/forces for pre-training.
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Misc. Atomistic Graph Datasets: Including Molecule3D, Protein Data Bank (PDB), and the Open Quantum Materials Database (OQMD).
Currently, AtomGen has pre-processed datasets for the S2EF pre-training task for OC20 and a mixed dataset of OC20, OC22, ODAC23, MPtrj, and SPICE. They have been uploaded to huggingface hub and can be accessed using the datasets API.
AtomGen supports a variety of models for training on atomistic graph datasets, including:
- SchNet
- TokenGT
- Uni-Mol+ (Modified)
Experimentation with pre-training tasks is facilitated through AtomGen, including:
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Structure to Energy & Forces: Predicting energies and forces for atomistic graphs.
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Masked Atom Modeling: Masking atoms and predicting their properties.
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Coordinate Denoising: Denoising atom coordinates.
These tasks are all facilitated through the DataCollatorForAtomModeling class and can be used simultaneously or individually.
The package can be installed using poetry:
python3 -m poetry install
source $(poetry env info --path)/bin/activateThe development environment can be set up using poetry. Hence, make sure it is installed and then run:
python3 -m poetry install
source $(poetry env info --path)/bin/activateIn order to install dependencies for testing (codestyle, unit tests, integration tests), run:
python3 -m poetry install --with test