We introduce MatterGen, a diffusion-based generative model that designs novel, stable inorganic materials across the periodic table with desired properties.

We propose CDVAE, a variational autoencoder that generates stable crystal structures by learning to denoise atom types, coordinates, and lattice parameters simultaneously.

We present CHGNet, a graph neural network pretrained on the Materials Project trajectory dataset, enabling rapid and accurate prediction of energies, forces, and magnetic moments.

We propose Uni-Mol, a universal molecular representation learning framework that directly operates on 3D molecular structures, significantly improving property prediction tasks.