Inverse Structural Design of Graphene/Boron Nitride Hybrids by Regressional GAN

TL;DR

This paper introduces a novel regressional GAN that enables inverse design of graphene/boron nitride hybrid materials with targeted bandgaps, significantly accelerating materials discovery by generating high-fidelity structures aligned with desired properties.

Contribution

The paper presents a new RGAN model combining supervised regressional CNN with traditional GANs for inverse material design, overcoming previous technical barriers.

Findings

Generated structures have bandgaps within ~10% MAEF of targets.

Structures follow the statistical distribution of real data.

The method accelerates discovery of 2D materials.

Abstract

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.