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Two dimensional (2D) materials such as graphene and transition metal dichalcogenides (TMDs) are promising for optical modulation, detection, and light emission since their material properties can be tuned on-demand via electrostatic doping1–21. The optical properties of TMDs have been shown to change drastically with doping in the wavelength range near the excitonic resonances22–26. However, little is known about the effect of doping on the optical properties of TMDs away from these resonances, where the material is transparent and therefore could be leveraged in photonic circuits. Here, we probe the electro-optic response of monolayer TMDs at near infrared (NIR) wavelengths (i.e. deep in the transparency regime), by integrating them on silicon nitride (SiN) photonic structures to induce strong light -matter interaction with the monolayer. We dope the monolayer to carrier densities of (7.2 ± 0.8) × 1013 cm-2, by electrically gating the TMD using an ionic liquid [P14+] [FAP-]. We show strong electro-refractive response in monolayer tungsten disulphide (WS2) at NIR wavelengths by measuring a large change in the real part of refractive index ∆n = 0.53, with only a minimal change in the imaginary part ∆k = 0.004. We demonstrate photonic devices based on electrostatically gated SiN-WS2 phase modulator with high efficiency ( ) of 0.8 V · cm. We show that the induced phase change relative to the change in absorption (i.e. ∆n/∆k) is approximately 125, that is significantly higher than the ones achieved in 2D materials at different spectral ranges and in bulk materials, commonly employed for silicon photonic modulators such as Si and III-V on Si, while accompanied by negligible insertion loss. Efficient phase modulators are critical for enabling large-scale photonic systems for applications such as Light Detection and Ranging (LIDAR), phased arrays, optical switching, coherent optical communication and quantum and optical neural networks27–30.
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Universal Ensemble‐Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures
Abstract Optical properties in solids, such as refractive index and absorption, hold vast applications ranging from solar panels to sensors, photodetectors, and transparent displays. However, first‐principles computation of optical properties from crystal structures is a complex task due to the high convergence criteria and computational cost. Recent progress in machine learning shows promise in predicting material properties, yet predicting optical properties from crystal structures remains challenging due to the lack of efficient atomic embeddings. Here, Graph Neural Network for Optical spectra prediction (GNNOpt) is introduced, an equivariant graph‐neural‐network architecture featuring universal embedding with automatic optimization. This enables high‐quality optical predictions with a dataset of only 944 materials. GNNOpt predicts all optical properties based on the Kramers‐Krönig relations, including absorption coefficient, complex dielectric function, complex refractive index, and reflectance. The trained model is applied to screen photovoltaic materials based on spectroscopic limited maximum efficiency and search for quantum materials based on quantum weight. First‐principles calculations validate the efficacy of the GNNOpt model, demonstrating excellent agreement in predicting the optical spectra of unseen materials. The discovery of new quantum materials with high predicted quantum weight, such as SiOs, which host exotic quasiparticles with multifold nontrivial topology, demonstrates the potential of GNNOpt in predicting optical properties across a broad range of materials and applications.
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- Award ID(s):
- 2345084
- PAR ID:
- 10641185
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Materials
- Volume:
- 36
- Issue:
- 46
- ISSN:
- 0935-9648
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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