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Anisotropies in conductivity measurements of bismuth point to the spontaneous breaking of intrinsic degeneracies in its electronic structure — and suggest there may be still more to learn from this well-studied material.
Reductionism, as a paradigm, is expired, and complexity, as a field, is tired. Data-based mathematical models of complex systems are offering a fresh perspective, rapidly developing into a new discipline: network science.
Is it possible for a metal to exist in a strictly two-dimensional system? This may seem trivial, but it is actually a longstanding problem. The electrical characteristics of an array of superconducting islands on a normal metal suggests that the answer could be 'yes'.
Vast amounts of data are available about complex technological systems and how we use them. These data provide the basis not only for mapping out connectivity patterns, but also for the study of dynamical phenomena, including epidemic outbreaks and routing of information through computer networks. This article reviews the fundamental tools for modelling such dynamical processes and discusses a number of applications.
Networks have proved to be useful representations of complex systems. Within these networks, there are typically a number of subsystems defined by only a subset of nodes and edges. Detecting these structures often provides important information about the organization and functioning of the overall network. Here, progress towards quantifying medium- and large-scale structures within complex networks is reviewed.
Aspects concerning the structure and behaviours of individual networks have been studied intensely in the past decade, but the exploration of interdependent systems in the context of complex networks has started only recently. This article reviews a general framework for modelling the percolation properties of interacting networks and the first results drawn from its study.
A completely ordered universe is as unexciting as an entirely disordered one. Interesting ‘complex’ phenomena arise in a middle ground. This article reviews the tools that have been developed to quantify structural complexity and to automatically discover patterns hidden between order and chaos.
Transforming a quantum system with high fidelity is usually a trade-off between an increase in speed—thereby minimizing decoherence—and robustness against fluctuating control parameters. Protocols at these two extreme limits are now demonstrated and compared using Bose–Einstein condensates in optical traps.
The unavoidable coupling between a quantum state and its environment leads to decoherence. Weak measurements—indirectly observing a quantum state without disturbing it—are now shown to be a useful tool for reducing or even nullifying the effects of decoherence.
An experimental technique based on Doppler velocimetry provides a detailed picture of electronic spins as they diffuse, drift and turn under the action of an electric field in a two-dimensional electron gas.
The realization of a single-particle Stirling engine pushes thermodynamics into stochastic territory where fluctuations dominate, and points towards a better understanding of energy transduction at the microscale.
