  Inverse design of materials with desired properties is currently laborious
and heavily relies on intuition of researchers through a trial-and-error
process. The massive combinational spaces due to the constituent elements and
their structural configurations are too overwhelming to be all searched even by
high-throughput computations. Herein, we demonstrated a novel regressional
generative adversarial network (RGAN) for inverse design of representative
two-dimensional materials, graphene and boron-nitride (BN) hybrids. A
significant novelty of the proposed RGAN is that it combines the supervised and
regressional convolutional neural network (CNN) with the traditional
unsupervised GAN, thus overcoming the common technical barrier in the
traditional GANs, which cannot generate data associated with given continuous
quantitative labels. The proposed RGAN enables to autonomously generate
graphene/BN hybrids with any given bandgaps. Moreover, the generated structures
exhibit high fidelity, yielding bandgaps within ~ 10% MAEF of the desired
bandgaps as cross-validated by density functional theory (DFT) calculations.
Further analysis by principle component analysis (PCA) and modified locally
linear embedding (MLLE) methods on the latent features encoded by the regressor
reveals that the generator has successfully generated structures that followed
the statistical distribution of the real structures. It implies the possibility
of the RGAN in recognizing physical rules hidden in the high-dimensional data.
This new inverse design methodology would speed up the discovery and
development of other 2D materials and beyond.
