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Abstract The mechanical exfoliation of naturally occurring layered materials has emerged as an easy and effective method for achieving ultrathin van der Waals (vdW) heterostructures with well-defined lattice orientations of the constituent two-dimensional (2D) material layers. Cylindrite is one such naturally occurring vdW heterostructure, where the superlattice is composed of alternating stacks of SnS2-like and PbS-like layers. Although the constituent 2D lattices are isotropic, inhomogeneous strain occurring from local atomic alignment for forcing the commensuration makes the cylindrite superlattice structurally anisotropic. Here, we demonstrate the highly anisotropic optical responses of cylindrite thin flakes induced by the anisotropic crystal structure, including angle-resolved polarized Raman scattering, linear dichroism, and polarization-dependent anisotropic third-harmonic generation. Our results provide a promising approach for identifying various natural vdW heterostructure-based 2D materials with tailored optical properties and can be harnessed for realizing anisotropic optical devices for on-chip photonic circuits and optical information processing.
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Prediction of Mohs Hardness with Machine Learning Methods Using Compositional Features
Hardness, or the quantitative value of resistance to permanent or plastic deformation, plays a crucial role in materials design for many applications, such as ceramic coatings and abrasives. Hardness testing is an especially useful method because it is nondestructive and simple to implement and gauge the plastic properties of a material. In this study, I proposed a machine, or statistical, learning approach to predict hardness in naturally occurring ceramic materials, which integrates atomic and electronic features from composition directly across a wide variety of mineral compositions and crystal systems. First, atomic and electronic features, such as van der Waals, covalent radii, and the number of valence electrons, were extracted from composition. The results showed that this proposed method is very promising for predicting Mohs hardness with F1-scores >0.85. The dataset in this study included modeling across a larger set of materials and hardness values, which have never been predicted in previous studies. Next, feature importances were used to identify the strongest contributions of these compositional features across multiple regimes of hardness. Finally, the models that were trained on naturally occurring ceramic minerals were applied to synthetic, artificially grown single crystal ceramics.
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- Award ID(s):
- 1647196
- PAR ID:
- 10187152
- Date Published:
- Journal Name:
- ACS symposium series
- Volume:
- 1326
- ISSN:
- 1947-5918
- Page Range / eLocation ID:
- 23-48
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1021/bk-2019-1326.ch002
