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Two-dimensional nonlinear optical materials predicted by network visualization

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Abstract

Two-dimensional (2D) materials with nonlinear optical (NLO) effects have emerged as promising candidates for nanoscale laser devices. However, only a few monolayers have been experimentally explored. Herein, starting from 258 compounds that have been predicted to be readily exfoliable, we built networks based on the optical properties of the compounds with machine learning and graph theory to illustrate the importance and connection of their elements. The results show that iodine, bromine, oxygen and chlorine play very important roles in these materials; metal chalcogenides also play a large role; and hydrogen, which is usually negligible in bulk crystals, may represent a breakthrough in 2D systems. The first-principles calculations are consistent with previous publications both theoretically and experimentally. This method can also be applied to other functional material portfolios.

Graphical abstract: Two-dimensional nonlinear optical materials predicted by network visualization

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Publication details

The article was received on 12 Dec 2018, accepted on 06 Feb 2019 and first published on 08 Feb 2019


Article type: Paper
DOI: 10.1039/C8ME00108A
Citation: Mol. Syst. Des. Eng., 2019, Advance Article

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    Two-dimensional nonlinear optical materials predicted by network visualization

    G. Yang and K. Wu, Mol. Syst. Des. Eng., 2019, Advance Article , DOI: 10.1039/C8ME00108A

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