  Two dimensional (2D) materials have emerged as promising functional materials
with many applications such as semiconductors and photovoltaics because of
their unique optoelectronic properties. While several thousand 2D materials
have been screened in existing materials databases, discovering new 2D
materials remains to be challenging. Herein we propose a deep learning
generative model for composition generation combined with random forest based
2D materials classifier to discover new hypothetical 2D materials. Furthermore,
a template based element substitution structure prediction approach is
developed to predict the crystal structures of a subset of the newly predicted
hypothetical formulas, which allows us to confirm their structure stability
using DFT calculations. So far, we have discovered 267,489 new potential 2D
materials compositions and confirmed twelve 2D/layered materials by DFT
formation energy calculation. Our results show that generative machine learning
models provide an effective way to explore the vast chemical design space for
new 2D materials discovery.
