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Two-Dimensional Nonlinear Optical Materials Predicted by Network Visualization

Abstract

Two-dimensional(2D) nonlinear optical(NLO) have emerged as promising candidates for nanoscale laser device. Yet only several monolayers have been experimentally explored. Here, starting from 258 compounds predicted easily exfoliable, we build networks based on their optical properties with machine learning and graph theory to illustrate the importance and connection of elements. The results shows iodine, bromine, oxygen and chlorine are playing very important roles here; metal chalcogenide also takes a big part; and hydrogen, which is usually negligible in bulk crystals, could be a break through in 2D system here. The first-principle calculated data are consistent with previous publications both theoretically and experimentally. This method can also apply in other functional materials portfolios.

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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, Accepted Manuscript

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    Two-Dimensional Nonlinear Optical Materials Predicted by Network Visualization

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

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