Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning.

Due to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials science data. Here, we introduce ARISE, a crystal-structure identification method based on Bayesian deep learning. As a major step forward, ARISE is robust to structural noise and can treat more than 100 crystal structures, a number that can be extended on demand.

Insightful classification of crystal structures using deep learning.

Computational methods that automatically extract knowledge from data are critical for enabling data-driven materials science. A reliable identification of lattice symmetry is a crucial first step for materials characterization and analytics. Current methods require a user-specified threshold, and are unable to detect average symmetries for defective structures.

Bandgap Engineering of Phosphorene by Laser Oxidation toward Functional 2D Materials.

We demonstrate a straightforward and effective laser pruning approach to reduce multilayer black phosphorus (BP) to few-layer BP under ambient condition. Phosphorene oxides and suboxides are formed and the degree of laser-induced oxidation is controlled by the laser power. Since the band gaps of the phosphorene suboxide depend on the oxygen concentration, this simple technique is able to realize localized band gap engineering of the thin BP.

Transport properties of pristine few-layer black phosphorus by van der Waals passivation in an inert atmosphere.

Ultrathin black phosphorus is a two-dimensional semiconductor with a sizeable band gap. Its excellent electronic properties make it attractive for applications in transistor, logic and optoelectronic devices. However, it is also the first widely investigated two-dimensional material to undergo degradation upon exposure to ambient air.