  Efficient algorithms to generate candidate crystal structures with good
stability properties can play a key role in data-driven materials discovery.
Here we show that a crystal diffusion variational autoencoder (CDVAE) is
capable of generating two-dimensional (2D) materials of high chemical and
structural diversity and formation energies mirroring the training structures.
Specifically, we train the CDVAE on 2615 2D materials with energy above the
convex hull $\Delta H_{\mathrm{hull}}< 0.3$ eV/atom, and generate 5003
materials that we relax using density functional theory (DFT). We also generate
14192 new crystals by systematic element substitution of the training
structures. We find that the generative model and lattice decoration approach
are complementary and yield materials with similar stability properties but
very different crystal structures and chemical compositions. In total we find
11630 predicted new 2D materials, where 8599 of these have $\Delta
H_{\mathrm{hull}}< 0.3$ eV/atom as the seed structures, while 2004 are within
50 meV of the convex hull and could potentially be synthesized. The relaxed
atomic structures of all the materials are available in the open Computational
2D Materials Database (C2DB). Our work establishes the CDVAE as an efficient
and reliable crystal generation machine, and significantly expands the space of
2D materials.
