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Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon Coupling in Hybrid Organic-Inorganic Perovskites 16

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Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon...

16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Kim, Dasom Hou, Jin Lee, Geon Agrawal, Ayush Kim, Sunghwan Zhang, Hao Bao, Di Baydin, Andrey Wu, Wenjing Tay, Fuyang Huang, Shengxi Hagenmüller, Daivd Chia, Elbert E. M. Kim, Dai-Sik Seo, Minah Mohite, Aditya D. Kono, Junichiro Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States Sensor System Research Center Korea Institute of Science and Technology Seoul02792 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Department of Chemical and Biomolecular Engineering Rice University HoustonTX77005 United States Ulsan44919 Korea Republic of Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Smalley-Curl Institute Rice University HoustonTX77005 United States Université de Strasbourg Cnrs Strasbourg67000 France KU-KIST Graduate School of Converging Science and Technology Korea University Seoul02841 Korea Republic of Department of Physics and Astronomy Rice University HoustonTX77005 United States

Methods for harnessing vibrational states are desired for phonon-based technologies. We realized ultrastrong coupling of two phonon modes in perovskite materials induced by ultrastrong coupling with a common photonic ... 详细信息

ISBN: (纸本)9798350372076

关键词： Organic-inorganic materials

Inverse design of optical switch with meta-learning

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Inverse design of optical switch with meta-learning

Conference on Lasers and Electro-Optics (CLEO)

作者： Beicheng Lou Jesse A Rodrıguez Benjamin Wang Mark Cappelli Shanhui Fan Department of Applied Physics Stanford University Stanford CA USA Department of Mechanical Engineering Stanford University Stanford CA USA Department of Electrical Engineering Ginzton Laboratory Stanford University Stanford CA USA

We introduce a novel way of designing active structure based on bi-level optimization, in analogy to meta-learning. We demonstrate our method with an example of compact optical switch design and discuss its broader ap... 详细信息

ISBN: (纸本)9781957171258

关键词： Inverse problems Microelectromechanical systems Modulation Nonlinear optical devices Optical devices Optical switching devices

Sensing beyond the exceptional point for high detectivity

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arXiv 2024年

作者： Li, Zhichao Prasad, Ciril S. Wang, Xielin Zhang, Ding Naik, Gururaj V. Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Program Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States

Exceptional point (EP)-based optical sensors exhibit exceptional sensitivity but poor detectivity due to their acute sensitivity to perturbations such as noise. When the optical budget is limited as in applications on mobile platforms, high detectivity might be equally important as high sensitivity. In such scenarios, off-EP sensing is advantageous where a slight loss in sensitivity can lead to a significant gain in detectivity. Here, we show that a passive parity-time symmetric plasmonic-photonic hybrid shows a peak in its detectivity off EP where the sensitivity is still very high – surpassing the sensitivities of equivalent fully-plasmonic and fully-photonic systems. We demonstrate the high detectivity of off-EP sensing of anti-mouse IgG protein using this plasmonic-photonic hybrid system. We report a sensitivity of 1.2 nm/nM while requiring a minimum optical budget of 1.3 nJ. Thus, non-Hermitian plasmonic-photonic hybrids could be the best class of nanophotonic sensors for real-world applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

Physically explainable statistical learning of flat bands in stoichiometric materials from the periodic table

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Physical Review Materials 2023年第6期7卷 064804-064804页

作者： Xiuying Zhang Yi-Ming Zhao Zhigang Song Lei Shen Department of Physics National University of Singapore 2 Science Drive 3 Singapore 117551 Singapore Department of Mechanical Engineering National University of Singapore 9 Engineering Drive 1 Singapore 117575 Singapore John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts 02138 USA

Flat-band materials (FBMs) serve as a platform for a variety of exotic properties and applications, such as strongly correlated states, topological states, and superconductivity. However, reported FBMs highly rely on materials engineering, such as Moiré lattices. Here, we demonstrate the acceleration of intrinsic FBM discovery using explainable statistical learning within a periodic table representation (PTR). Our model achieves validation accuracies of 0.81–0.97 for X2YZ full-Heusler alloys across three different databases and 0.84 for ABC3 perovskites. Our interpretable model and statistical analysis reveal several important valence electron-related features for FBMs, supported by atomic orbital hybridization theory. We further discuss various physical properties and applications strongly associated with flat bands, including topology, thermal conductivity, electron-phonon coupling, and superconductivity. Finally, we predict 25 high-potential, previously unreported flat-band Heusler alloys using the PTR model, validated by first-principles calculations.

关键词： Electronic structure Neuronal networks Heusler alloy Deep learning Density functional calculations

Interlayer couplings in cuprates: structural origins, analytical forms, and structural estimators

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arXiv 2024年

作者： Jin, Zheting Ismail-Beigi, Sohrab Department of Applied Physics Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States

We quantitatively identify the multiple distinct microscopic mechanisms contributing to effective interlayer couplings (EICs) by performing first-principle calculations for two prototype superconducting cuprate families, pristine and doped Bi2Sr2CaCuO2O8+x and PrxY1−xBa2Cu3O7. The major mechanisms are mediated by interlayer oxygen pσ-pσ and pz-pz hoppings as well as interlayer copper dz2-oxygen pσ hoppings. Furthermore, we show how EICs are closely related to structural distortions such as layer bucklings and bond length changes. This allows us to provide analytical formulae that permit direct estimation of the key interatomic hoppings and the EICs based only on the crystal structure. Finally, we benchmark our method on YBa2Cu3O7 to estimate the strength and anisotropy of the EIC. © 2024, CC BY.

关键词： Strontium compounds

Efficient Pumping of Spectral Holes in a Tm3+: YAG Crystal for Broadband Quantum Optical Storage

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arXiv 2024年

作者： Lei, Yisheng Li, Zongfeng Hosseini, Mahdi Department of Electrical and Computer Engineering and Applied Physics Program Northwestern University EvanstonIL60208 United States Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Here, we report a storage efficiency greater than 28% in a Tm3+: YAG crystal in elevated temperatures and without compromising the memory bandwidth. Using various pumping and optimization techniques, we demonstrate multi-frequency window storage with a high memory bandwidth of 630 MHz. Moreover, we propose a general method for large-bandwidth atomic-frequency memory with non-Kramers rare-earth-ion (REI) in solids enabling significantly higher storage efficiency and bandwidth. Our study advances the practical applications of quantum memory devices based on REI-doped crystals. © 2024, CC BY.

关键词： Optical pumping

Spectral Graph Theoretic Methods for Enhancing Network Robustness in Robot Localization

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Spectral Graph Theoretic Methods for Enhancing Network Robus...

IEEE Conference on Decision and Control

作者： Neelkamal Somisetty Harsha Nagarajan Swaroop Darbha Department of Mechanical Engineering Texas A & M University College Station TX USA Applied Mathematics and Plasma Physics (T-5) Los Alamos National Laboratory Los Alamos NM USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper addresses the optimization of edge-weighted networks by maximizing algebraic connectivity to enhance network robustness. Motivated by the need for precise robot position estimation in cooperative localization and pose-graph sparsification in Simultaneous Localization and Mapping (SLAM), the algebraic connectivity maximization problem is formulated as a Mixed Integer Semi-Definite program (MISDP), which is NP-hard. Leveraging spectral graph theoretic methods, specifically Cheeger’s inequality, this work introduces novel “Cheeger cuts” to strengthen and efficiently solve medium-scale MISDPs. Further, a new Mixed Integer Linear program (MILP) is developed for efficiently computing Cheeger cuts, implemented within an outer-approximation algorithm for solving the MISDP. A greedy k-opt heuristic is also presented, producing high-quality solutions that serve as valid lower bounds for Cheeger cuts. Comprehensive numerical analyses demonstrate the efficacy of strengthened cuts via substantial improvements in run times on synthetic and realistic robot localization datasets.

关键词： Location awareness Lower bound Visualization Simultaneous localization and mapping Robustness Robot localization Computational efficiency Optimization Robots Standards

Adaptive multi-altitude search and sampling of sparsely distributed natural phenomena

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Adaptive multi-altitude search and sampling of sparsely dist...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jessica E. Todd Seth McCammon Yogesh Girdhar Nicholas Roy Dana Yoerger Department of Aeronautics and Astronautics MIT-WHOI Joint Program in Applied Ocean Science and Engineering at the Woods Hole Oceanographic Institution (WHOI) Massachusetts Institute of Technology (MIT) Applied Ocean Physics and Engineering Department Woods Hole Oceanographic Institution Woods Hole MA USA Department of Aeronautics and Astronautics Massachusetts Institute of Technology

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this paper, we propose a novel method for autonomously seeking out sparsely distributed targets in an unknown underwater environment. Our Sparse Adaptive Search and Sample (SASS) algorithm mixes low-altitude observations of discrete targets with high-altitude observations of the surrounding substrates. By using prior information about the distribution of targets across substrate types in combination with belief modelling over these substrates in the environment, high-altitude observations provide information that allows SASS to quickly guide the robot to areas with high target densities. A maximally informative path is autonomously constructed online using Monte Carlo Tree Search with a novel acquisition function to guide the search to maximise observations of unique targets. We demonstrate our approach in a set of simulated trials using a novel generative species model. SASS consistently outperforms the canonical boustrophedon planner by up to 36% in seeking out unique targets in the first 75-90% of time it takes for a boustrophedon survey. Additionally, we verify the performance of SASS on two real world coral reef datasets.

关键词： Surveys Adaptation models Monte Carlo methods Adaptive systems Marine vegetation Substrates Intelligent robots

Machine-Learning-Enhanced Quantum Optical Storage in Solids

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arXiv 2024年

作者： Lei, Yisheng An, Haechan Li, Zongfeng Hosseini, Mahdi Department of Electrical and Computer Engineering Applied Physics Program Northwestern University EvanstonIL60208 United States Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Solid-state quantum memories can provide broadband storage, but they primarily suffer from low storage efficiency. We use passive optimization and machine learning techniques to demonstrate nearly a 6-fold enhancement in quantum memory efficiency. In this regime, we demonstrate coherent and single-photon-level storage with a high signal-to-noise ratio. The optimization technique presented here can be applied to most solid-state quantum memories to significantly improve the storage efficiency without compromising the memory bandwidth. © 2024, CC BY.

关键词： Signal to noise ratio