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Hyperbolic phonon polaritons (HPhPs) enable strong confinements, low losses, and intrinsic beam steering capabilities determined by the refractive index anisotropy—providing opportunities from hyperlensing to flat optics and other applications. Here, two scanning-probe techniques, photothermal induced resonance (PTIR) and scattering-type scanning near-field optical microscopy (s-SNOM), are used to map infrared (6.4–7.4 µm) HPhPs in large (up to 120 × 250 µm2) near-monoisotopic (>99% 10B) hexagonal boron nitride (hBN) flakes. Wide (≈40 µm) PTIR and s-SNOM scans on such large flakes avoid interference from polaritons launched from different asperities (edges, folds, surface defects, etc.) and together with Fourier analyses (0.05 µm−1 resolution) enable precise measurements of HPhP lifetimes (up to ≈4.2 ps) and propagation lengths (up to ≈25 and ≈17 µm for the first- and second-order branches, respectively). With respect to naturally abundant hBN, we report an eightfold improved, record-high (for hBN) propagating figure of merit (i.e., with both high confinement and long lifetime) in ≈99% 10B hBN, achieving, finally, theoretically predicted values. We show that wide near-field scans critically enable accurate estimates of the polaritons’ lifetimes and propagation lengths and that the incidence angle of light, with respect to both the sample plane and the flake edge, needs to be considered to extract correctly the dispersion relation from the near-field polaritons maps. Overall, the measurements and data analyses employed here elucidate details pertaining to polaritons’ propagation in isotopically enriched hBN and pave the way for developing high-performance HPhP-based devices. 

Abstract The anisotropy of hexagonal boron nitride (hBN) gives rise to hyperbolic phonon-polaritons (HPhPs), notable for their volumetric frequency-dependent propagation and strong confinement. For frustum (truncated nanocone) structures, theory predicts five, high-order HPhPs, sets, but only one set was observed previously with far-field reflectance and scattering-type scanning near-field optical microscopy. In contrast, the photothermal induced resonance (PTIR) technique has recently permitted sampling of the full HPhP dispersion and observing such elusive predicted modes; however, the mechanism underlying PTIR sensitivity to these weakly-scattering modes, while critical to their understanding, has not yet been clarified. Here, by comparing conventional contact- and newly developed tapping-mode PTIR, we show that the PTIR sensitivity to those weakly-scattering, high-Q (up to ≈280) modes is, contrary to a previous hypothesis, unrelated to the probe operation (contact or tapping) and is instead linked to PTIR ability to detect tip-launched dark, volumetrically-confined polaritons, rather than nanostructure-launched HPhPs modes observed by other techniques. Furthermore, we show that in contrast with plasmons and surface phonon-polaritons, whose Q -factors and optical cross-sections are typically degraded by the proximity of other nanostructures, the high- Q HPhP resonances are preserved even in high-density hBN frustum arrays, which is useful in sensing and quantum emission applications. 

Abstract The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov .