The investigation of twisted stacked few‐layer MoS2has revealed novel electronic, optical, and vibrational properties over an extended period. For the successful integration of twisted stacked few‐layer MoS2into a wide range of applications, it is crucial to employ a noninvasive, versatile technique for characterizing the layered architecture of these complex structures. In this work, we introduce a machine learning‐assisted low‐frequency Raman spectroscopy method to characterize the twist angle of few‐layer stacked MoS2samples. A feedforward neural network (FNN) is utilized to analyze the low‐frequency breathing mode as a function of the twist angle. Moreover, using finite difference method (FDM) and density functional theory (DFT) calculations, we show that the low‐frequency Raman spectra of MoS2are mainly influenced by the effect of the nearest and second nearest layers. A new improved linear chain model (TA‐LCM) with taking the twist angle into the consideration is developed to understand the interlayer breathing modes of stacked few‐layer MoS2. This approach can be extended to other 2D materials systems and provides an intelligent way to investigate naturally stacked and twisted interlayer interactions.
Euclidean geometry is the fundamental mathematical framework of classical crystallography. Traditionally, layered materials are grown on flat substrates; growing Euclidean crystals on non-Euclidean surfaces has rarely been studied. We present a general model describing the growth of layered materials with screw-dislocation spirals on non-Euclidean surfaces and show that it leads to continuously twisted multilayer superstructures. This model is experimentally demonstrated by growing supertwisted spirals of tungsten disulfide (WS2) and tungsten diselenide (WSe2) draped over nanoparticles near the centers of spirals. Microscopic structural analysis shows that the crystal lattice twist is consistent with the geometric twist of the layers, leading to moiré superlattices between the atomic layers.
more » « less- NSF-PAR ID:
- 10198882
- Publisher / Repository:
- American Association for the Advancement of Science (AAAS)
- Date Published:
- Journal Name:
- Science
- Volume:
- 370
- Issue:
- 6515
- ISSN:
- 0036-8075
- Page Range / eLocation ID:
- p. 442-445
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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