Search for: All records

Hexagonal boron nitride (h-BN) has emerged as a promising platform for generating room temperature single photons exhibiting high brightness and spin-photon entanglement. However, improving emitter purity, stability, and scalability remains a challenge for quantum technologies. Here, we demonstrate highly pure and stable single-photon emitters (SPEs) in h-BN by directly growing carbon-doped, centimeter-scale h-BN thin films using the pulsed laser deposition (PLD) method. These SPEs exhibit room temperature operation with polarized emission, achieving ag⁽²⁾(0) value of 0.015, which is among the lowest reported for room temperature SPEs and the lowest achieved for h-BN SPEs. It also exhibits high brightness (~0.5 million counts per second), remarkable stability during continuous operation (>15 min), and a Debye-Waller factor of 45%. First-principles calculations reveal unique carbon defects responsible for these properties, enabled by PLD’s low-temperature synthesis and in situ doping. Our results demonstrate an effective method for large-scale production of high-purity, stable SPEs in h-BN, enabling robust quantum optical sources for various quantum applications. 

Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website:https://pages.nist.gov/jarvis_leaderboard/