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Predicting synthesizable multi-functional edge reconstructions in two-dimensional transition metal dichalcogenides

Guoxiang Hu, Victor Fung, Xiahan Sang, Raymond R. Unocic, Panchapakesan Ganesh

2020npj Computational Materials29 citationsDOIOpen Access PDF

Abstract

Abstract Two-dimensional (2D) transition metal dichalcogenides (TMDCs) have attracted tremendous interest as functional materials due to their exceptionally diverse and tunable properties, especially in their edges. In addition to the conventional armchair and zigzag edges common to hexagonal 2D materials, more complex edge reconstructions can be realized through careful control over the synthesis conditions. However, the whole family of synthesizable, reconstructed edges remains poorly studied. Here, we develop a computational approach integrating ensemble-generation, force-relaxation, and electronic-structure calculations to systematically and efficiently discover additional reconstructed edges and screen their functional properties. Using MoS 2 as a model system, we screened hundreds of edge-reconstruction to discover over 160 reconstructed edges to be more stable than the conventional ones. More excitingly, we discovered nine new synthesizable reconstructred edges with record thermodynamic stability, in addition to successfully reproducing three recently synthesized edges. We also find our predicted reconstructed edges to have multi-functional properties—they show near optimal hydrogen evolution activity over the conventional edges, ideal for catalyzing hydrogen-evolution reaction (HER) and also exhibit half-metallicity with a broad variation in magnetic moments, making them uniquely suitable for nanospintronic applications. Our work reveals the existence of a wide family of synthesizable, reconstructed edges in 2D TMDCs and opens a new materials-by-design paradigm of ‘intrinsic’ edge engineering multifunctionality in 2D materials.

Topics & Concepts

ZigzagEnhanced Data Rates for GSM EvolutionMaterials scienceTransition metalDensity functional theoryStability (learning theory)NanotechnologyComputer scienceComputational chemistryChemistryMathematicsGeometryCatalysisMachine learningBiochemistryTelecommunications2D Materials and ApplicationsMXene and MAX Phase MaterialsGraphene research and applications