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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.29298 |
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| _version_ | 1866911556578574336 |
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| author | Hazra, Soumyadip Dey, Sraboni Kayal, Arijit Shah, Narendra Nadarajan, Renjith Mitra, Joy |
| author_facet | Hazra, Soumyadip Dey, Sraboni Kayal, Arijit Shah, Narendra Nadarajan, Renjith Mitra, Joy |
| contents | Wrinkles and nanobubbles are an integral and often unavoidable part of integrating 2D van der Waals semiconductors into actual device architectures. Despite their ubiquitous nature, quantitative correlation between such spatially non-uniform strain and modifications to the local electronic structure remains challenging. Here, density functional theory is combined with a recurrent neural network to reconstruct the local electronic structure of monolayer MoS2 from strain maps derived from atomic force microscopy (AFM) topography and Raman spectral maps. The analysis reveals that biaxial bending induced strain is significantly more effective than both uniaxial bending or in-plane strain in modifying electronic and dielectric properties. A ~ 0.35% strain induced by biaxial bending results in ~ 22% reduction in band gap and ~ 7% increase in dielectric constant, compared to a ~ 5% reduction in band gap and ~ 1% increase in dielectric constant under comparable uniaxial bending. The modified band structure reveals band edge states that concentrate charge in regions of high curvature or strain. While conductive AFM measurements indicate increased local conductance (carrier density) at wrinkles and nanobubbles, the spatial band gap maps predicted by the model are validated against experimental photoluminescence peak energy maps. The results indicate that strained features like wrinkles and nanobubbles commonly present in real devices influence the band gap, carrier distribution, and dielectric response, which favourably affects electrical transport in such systems. The framework developed here can be readily extended to other 2D materials and heterostructures, offering a computationally efficient route for studying and exploiting strain effects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29298 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine Learning Assisted Reconstruction of Local Electronic Structure of Non-Uniformly Strained MoS2 Hazra, Soumyadip Dey, Sraboni Kayal, Arijit Shah, Narendra Nadarajan, Renjith Mitra, Joy Materials Science Optics Wrinkles and nanobubbles are an integral and often unavoidable part of integrating 2D van der Waals semiconductors into actual device architectures. Despite their ubiquitous nature, quantitative correlation between such spatially non-uniform strain and modifications to the local electronic structure remains challenging. Here, density functional theory is combined with a recurrent neural network to reconstruct the local electronic structure of monolayer MoS2 from strain maps derived from atomic force microscopy (AFM) topography and Raman spectral maps. The analysis reveals that biaxial bending induced strain is significantly more effective than both uniaxial bending or in-plane strain in modifying electronic and dielectric properties. A ~ 0.35% strain induced by biaxial bending results in ~ 22% reduction in band gap and ~ 7% increase in dielectric constant, compared to a ~ 5% reduction in band gap and ~ 1% increase in dielectric constant under comparable uniaxial bending. The modified band structure reveals band edge states that concentrate charge in regions of high curvature or strain. While conductive AFM measurements indicate increased local conductance (carrier density) at wrinkles and nanobubbles, the spatial band gap maps predicted by the model are validated against experimental photoluminescence peak energy maps. The results indicate that strained features like wrinkles and nanobubbles commonly present in real devices influence the band gap, carrier distribution, and dielectric response, which favourably affects electrical transport in such systems. The framework developed here can be readily extended to other 2D materials and heterostructures, offering a computationally efficient route for studying and exploiting strain effects. |
| title | Machine Learning Assisted Reconstruction of Local Electronic Structure of Non-Uniformly Strained MoS2 |
| topic | Materials Science Optics |
| url | https://arxiv.org/abs/2603.29298 |