AtomGen Documentation#
Welcome to the documentation for AtomGen, a toolkit for atomistic graph pre-training and generative modeling. AtomGen empowers researchers and developers with tools to explore, experiment, and innovate in the realm of atomistic graph analysis.
Overview#
AtomGen provides a robust framework for handling atomistic graph datasets, training various models, and experimenting with different pre-training tasks. 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.
Datasets#
AtomGen facilitates the aggregation and standardization of datasets, including but not limited to:
S2EF Datasets: Aggregated from multiple sources such as OC20, OC22, ODAC23, MPtrj, and SPICE with structures and energies for pre-training.
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.
Models#
AtomGen supports a variety of models for training on atomistic graph datasets, including:
SchNet
TokenGT
Uni-Mol+ (Modified)
Tasks#
Experimentation with pre-training tasks is facilitated through AtomGen, including:
Structure to Energy & Forces: Predicting energies and forces for atomistic graphs.
Masked Atom Modeling: Masking atoms and predicting their properties.
Coordinate Denoising: Denoising atom coordinates.
These tasks are all facilitated through the DataCollatorForAtomModeling
class and can be used simultaneously or individually.