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. While being trained on ideal structures only, ARISE correctly characterizes strongly perturbed single- and polycrystalline systems, from both synthetic and experimental resources. The probabilistic nature of the Bayesian-deep-learning model allows to obtain principled uncertainty estimates, which are found to be correlated with crystalline order of metallic nanoparticles in electron tomography experiments. Applying unsupervised learning to the internal neural-network representations reveals grain boundaries and (unapparent) structural regions sharing easily interpretable geometrical properties. This work enables the hitherto hindered analysis of noisy atomic structural data from computations or experiments.

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. Here, we propose a machine learning-based approach to automatically classify structures by crystal symmetry. First, we represent crystals by calculating a diffraction image, then construct a deep learning neural network model for classification. Our approach is able to correctly classify a dataset comprising more than 100,000 simulated crystal structures, including heavily defective ones. The internal operations of the neural network are unraveled through attentive response maps, demonstrating that it uses the same landmarks a materials scientist would use, although never explicitly instructed to do so. Our study paves the way for crystal structure recognition of-possibly noisy and incomplete-three-dimensional structural data in big-data materials science.

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. Micropatterns of few-layer phosphorene suboxide flakes with unique optical and fluorescence properties are created. Remarkably, some of these suboxide flakes display long-term (up to 2 weeks) stability in ambient condition. Comparing against the optical properties predicted by first-principle calculations, we develop a "calibration" map in using focused laser power as a handle to tune the band gap of the BP suboxide flake. Moreover, the surface of the laser patterned region is altered to be sensitive to toxic gas by way of fluorescence contrast. Therefore, the multicolored display is further demonstrated as a toxic gas monitor. In addition, the BP suboxide flake is demonstrated to exhibit higher drain current modulation and mobility comparable to that of the pristine BP in the electronic application.

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. Therefore a passivation method is required to study the intrinsic material properties, understand how oxidation affects the physical properties and enable applications of phosphorene. Here we demonstrate that atomically thin graphene and hexagonal boron nitride can be used for passivation of ultrathin black phosphorus. We report that few-layer pristine black phosphorus channels passivated in an inert gas environment, without any prior exposure to air, exhibit greatly improved n-type charge transport resulting in symmetric electron and hole transconductance characteristics.