  Two-dimensional (2D) materials have wide applications in superconductors,
quantum, and topological materials. However, their rational design is not well
established, and currently less than 6,000 experimentally synthesized 2D
materials have been reported. Recently, deep learning, data-mining, and density
functional theory (DFT)-based high-throughput calculations are widely performed
to discover potential new materials for diverse applications. Here we propose a
generative material design pipeline, namely material transformer
generator(MTG), for large-scale discovery of hypothetical 2D materials. We
train two 2D materials composition generators using self-learning neural
language models based on Transformers with and without transfer learning. The
models are then used to generate a large number of candidate 2D compositions,
which are fed to known 2D materials templates for crystal structure prediction.
Next, we performed DFT computations to study their thermodynamic stability
based on energy-above-hull and formation energy. We report four new
DFT-verified stable 2D materials with zero e-above-hull energies, including
NiCl$_4$, IrSBr, CuBr$_3$, and CoBrCl. Our work thus demonstrates the potential
of our MTG generative materials design pipeline in the discovery of novel 2D
materials and other functional materials.
