Frustration, or the competition between interacting components of a network, is often responsible for the emergent complexity of many-body systems. For instance, frustrated magnetism is a hallmark of poorly understood systems such as quantum spin liquids, spin glasses, and spin ices, whose ground states can be massively degenerate and carry high degrees of quantum entanglement. Here, we engineer frustrated antiferromagnetic interactions between spins stored in a crystal of up to 16 trapped (171)Yb(+) atoms. We control the amount of frustration by continuously tuning the range of interaction and directly measure spin correlation functions and their coherent dynamics. This prototypical quantum simulation points the way toward a new probe of frustrated quantum magnetism and perhaps the design of new quantum materials.
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http://dx.doi.org/10.1126/science.1232296 | DOI Listing |
Biomed Phys Eng Express
January 2025
Xi'an Jiaotong University, No.28 Xianning West Road, Xi'an, Shaanxi 710049, P.R. China, Xi'an, 710049, CHINA.
The optimal method for three-dimensional thermal imaging within cells involves collecting intracellular temperature responses while simultaneously obtaining corresponding 3D positional information. Current temperature measurement techniques based on the photothermal properties of quantum dots face several limitations, including high cytotoxicity and low fluorescence quantum yields. These issues affect the normal metabolic processes of tumor cells.
View Article and Find Full Text PDFNanotechnology
January 2025
Xidian University, Xi'an 710071, China, Xi'an, Xian, Shaanxi, 710126, CHINA.
Anti-ambipolar transistors (AAT) are considered as a breakthrough technology in the field of electronics and optoelectronics, which is not only widely used in diverse logic circuits, but also crucial for the realization of high-performance photodetectors. The anti-ambipolar characteristics arising from the gate-tunable energy band structure can produce high-performance photodetection at different gate voltages. As a result, this places higher demands on the parametric driving range (ΔVg) and peak-to-valley ratio (PVR) of the AAT.
View Article and Find Full Text PDFACS Nano
January 2025
Department of Chemistry, School of Science and Key Laboratory for Quantum Materials of Zhejiang Province, Research Center for Industries of the Future, Westlake University, Hangzhou 310030, China.
In our previous studies of metal nanoparticle growth, we have come to realize that the dynamic interplay between ligand passivation and metal deposition, as opposed to static facet control, is responsible for focused growth at a few active sites. In this work, we show that the same underlying principle could be applied to a very different system and explain the abnormal growth modes of liquid nanoparticles. In such a liquid active surface growth (LASG), the interplay between droplet expansion and simultaneous silica shell encapsulation gives rise to an active site of growth, which eventually becomes the long necks of nanobottles.
View Article and Find Full Text PDFJ Phys Chem A
January 2025
Université Paris-Saclay, CNRS, Institut des Sciences Moléculaires d'Orsay UMR 8214, 91405 Orsay, France.
This study deals with the understanding of hydrogen atom scattering from graphene, a process critical for exploring C-H bond formation and energy transfer during atom surface collision. In our previous work [Shi, L.; 2023, 159, 194102], starting from a cell with 24 carbon atoms treated periodically, we have achieved quantum dynamics (QD) simulations with a reduced-dimensional model (15D) and a simulation in full dimensionality (75D).
View Article and Find Full Text PDFJ Chem Theory Comput
January 2025
Lehrstuhl für Theoretische Chemie II, Ruhr-Universität Bochum, Bochum 44780, Germany.
Training accurate machine learning potentials requires electronic structure data comprehensively covering the configurational space of the system of interest. As the construction of this data is computationally demanding, many schemes for identifying the most important structures have been proposed. Here, we compare the performance of high-dimensional neural network potentials (HDNNPs) for quantum liquid water at ambient conditions trained to data sets constructed using random sampling as well as various flavors of active learning based on query by committee.
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