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Aims to provide useful high-level interfaces that make ML for materials science as easy as possible.

15TB collection of 16 large-scale numerical simulation datasets spanning fluid dynamics, MHD, astrophysics, biological systems, and acoustic scattering, with unified PyTorch dataloaders and benchmarks for training foundation models on physical sciences (Polymathic AI, NeurIPS 2024)

Foundation model jointly trained on single-cell and spatial transcriptomics data, enabling unified representation learning across cellular and tissue spatial contexts for cell type prediction, spatial domain inference, and cross-modal integration (theislab, bioRxiv 2024, 164+ stars)

State-specific protein-ligand complex structure prediction with a multi-scale deep generative model, enabling conformational state-aware modeling of molecular interactions (329+ stars, 2024)

Library with several compositional and structural material descriptors, along with a few pre-trained neural network models of material properties.

A Deep Learning Library for Compound and Protein Modeling DTI, Drug Property, PPI, DDI, Protein Function Prediction.

Psi4-based reference implementations and Jupyter notebook-based tutorials for foundational quantum chemistry methods.