Angelo Ziletti

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.

Creating a Stable Oxide at the Surface of Black Phosphorus.

The stability of the surface of in situ cleaved black phosphorus crystals upon exposure to atmosphere is investigated with synchrotron-based photoelectron spectroscopy. After 2 days atmosphere exposure a stable subnanometer layer of primarily P2O5 forms at the surface. The work function increases by 0.1 eV from 3.9 eV for as-cleaved black phosphorus to 4.0 eV after formation of the 0.4 nm thick oxide, with phosphorus core levels shifting by <0.1 eV. The results indicate minimal charge transfer, suggesting that the oxide layer is suitable for passivation or as an interface layer for further dielectric deposition.

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.Classifying crystal structures using their space group is important to understand material properties, but the process currently requires user input. Here, the authors use machine learning to automatically classify more than 100,000 simulated perfect and defective crystal structures.