相关论文: Tunable stochasticity in an artificial spin networ…

At the interface between two distinct materials desirable properties, such as superconductivity, can be greatly enhanced, or entirely new functionalities may emerge. Similar to in artificially engineered heterostructures, clean functional…

Elastic metamaterials are often designed for a single permanent function. We explore the possibility of altering a material's function repeatedly through a self-organization, "training" process, controlled by applied strains. We show that…

We propose a novel approach for efficient tuning of the transmission characteristics of metamaterials through a continuous adjustment of the lattice structure, and confirm it experimentally in the microwave range. The concept is rather…

Many modern applications of the artificial neural networks ensue large number of layers making traditional digital implementations increasingly complex. Optical neural networks offer parallel processing at high bandwidth, but have the…

Mechanical metamaterials are artificial composites with tunable advanced mechanical properties. Particularly interesting types of mechanical metamaterials are flexible metamaterials, which harness internal rotations and instabilities to…

Metamaterials are artificial composite structures designed for controlling waves or fields, and exhibit interaction phenomena that are unexpected on the basis of their chemical constituents. These phenomena are encoded in effective material…

Structures with artificial mechanical properties, often called mechanical metamaterials, exhibit divergent yet tunable performance. Various types of mechanical metamaterials have been proposed, which harness light or magnetic interactions,…

The ability of mechanical systems to perform basic computations has gained traction over recent years, providing an unconventional alternative to digital computing in off grid, low power, and severe environments which render the majority of…

Simple local interactions can cause primitive building blocks to self-assemble into complex and functional patterns. However, even for a small number of blocks, there exist a vast number of possible configurations that are plausible,…

Artificial spin ice, arrays of strongly interacting nanomagnets, are complex magnetic systems with many emergent properties, rich microstate spaces, intrinsic physical memory, high-frequency dynamics in the GHz range and compatibility with…

Nonlinear metamaterials with tailored mechanical properties have applications in engineering, medicine, robotics, and beyond. While modeling their macromechanical behavior is challenging in itself, finding structure parameters that lead to…

Diffusion metamaterials with artificial spatial structures have significant potential in controlling energy and mass transfer. Those static structures may lead to functionality and tunability constraints, impeding the application scope of…

Wave-based platforms for novel unconventional computing approaches like neuromorphic computing require a well-defined, but adjustable flow of wave information combined with non-volatile data storage elements to implement weights which allow…

Auxetics refers to structures or materials with a negative Poisson's ratio, thereby capable of exhibiting counter-intuitive behaviors. Herein, auxetic structures are exploited to design mechanically tunable metamaterials in both planar and…

Electric control over magnetic interactions at the level of individual spins is relevant for a variety of quantum applications, such as qubits, memory and sensor functionality. We show here that spin lattices and magnon gaps can be…

Artificial Neural Networks were recently shown to be an efficient representation of highly-entangled many-body quantum states. In practical applications, neural-network states inherit numerical schemes used in Variational Monte Carlo, most…

Synaptic plasticity allows cortical circuits to learn new tasks and to adapt to changing environments. How do cortical circuits use plasticity to acquire functions such as decision-making or working memory? Neurons are connected in complex…

The exploration of intelligent machines has recently spurred the development of physical neural networks, a class of intelligent metamaterials capable of learning, whether in silico or in situ, from observed data. In this study, we…

Metamaterials are a new generation of advanced materials, exhibiting engineered microstructures that enable customized material properties not found in nature. The dynamics of metamaterials are particularly fascinating, promising the…

Precise control of magnetic domain formation at the nanoscale remains constrained by stochastic defect-mediated and unstable pinning, limiting scalability and reproducibility in spintronic architectures. Here we demonstrate that spatially…