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.Comment: Nature Communications, in press (2018

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.B.O. acknowledges support by the National Research Foundation, Prime Minister's Office, Singapore under its Competitive Research Programme (CRP Award No. NRF-CRP9-2011-3) and the SMF-NUS Research Horizons Award 2009-Phase II. A.H.C.N. acknowledges the NRF-CRP award 'Novel 2D materials with tailored properties: beyond graphene'. The calculations were performed at the GRC computing facilities. A.Z. and D.F.C. acknowledge the NSF grant CHE-1301157. (NRF-CRP9-2011-3 - National Research Foundation, Prime Minister's Office, Singapore under its Competitive Research Programme (CRP); SMF-NUS Research Horizons Award-Phase II; NRF-CRP award 'Novel 2D materials with tailored properties: beyond graphene'; CHE-1301157 - NSF)Published versio

Big-Data-Driven Materials Science and its FAIR Data Infrastructure

This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR -- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer 2018/201

Phosphorene: Fabrication, Properties and Applications

Phosphorene, the single- or few-layer form of black phosphorus, was recently rediscovered as a twodimensional layered material holding great promise for applications in electronics and optoelectronics. Research into its fundamental properties and device applications has since seen exponential growth. In this Perspective, we review recent progress in phosphorene research, touching upon topics on fabrication, properties, and applications; we also discuss challenges and future research directions. We highlight the intrinsically anisotropic electronic, transport, optoelectronic, thermoelectric, and mechanical properties of phosphorene resulting from its puckered structure in contrast to those of graphene and transition-metal dichalcogenides. The facile fabrication and novel properties of phosphorene have inspired design and demonstration of new nanodevices; however, further progress hinges on resolutions to technical obstructions like surface degradation effects and non-scalable fabrication techniques. We also briefly describe the latest developments of more sophisticated design concepts and implementation schemes that address some of the challenges in phosphorene research. It is expected that this fascinating material will continue to offer tremendous opportunities for research and development for the foreseeable future.Comment: invited perspective for JPC