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We study a model of mesoscale superconducting puddles in a metal, represented as dynamical impurities interacting with a finite number of electronic channels via Andreev and normal scattering. We identify conditions under which the collection of puddles make a T-linear contribution to the resistivity and a T ln(1/T ) to the specific heat and thermopower. This behavior emerges in an intermediate temperature range that extends from an upper energy scale set by the renormalized charging energy of the puddles, and down to an exponentially small scale associated with a charge-Kondo crossover, provided that the number of electronic channels interacting with the puddle is large. The phenomenology of our model resembles the apparent extended strange metal regime observed in overdoped cuprates which exhibits T-linear resistivity at low T over a finite range of doping. We also propose to engineer a strange metal from suitably designed superconducting grains in a metallic matrix. 

A quantum spin liquid (QSL) is an exotic insulating phase with emergent gauge fields and fractionalized excitations. However, the unambiguous demonstration of the existence of a QSL in a “nonengineered” microscopic model (or in any material) remains challenging. Here, using numerically exact sign-problem-free quantum Monte Carlo simulations, we show that a QSL arises in a nonengineered electron–phonon model. Specifically, we investigate the ground-state phase diagram of the bond Su–Schrieffer–Heeger model on a 2D triangular lattice at (one electron per site), which we show includes a QSL phase which is fully gapped, exhibits no symmetry-breaking order, and supports deconfined fractionalized holon excitations. This suggests promising routes for finding QSLs in realistic materials and high-T_csuperconductivity by lightly doping them. 

By explicit microscopic construction involving a mapping to a quantum vertex model subject to the “ice rule,” we show that an electronically “trivial” band insulator with suitable vibrational (phonon) degrees of freedom can host a “resonating valence-bond” state—a quantum phase with emergent gauge fields. This type of band insulator is identifiable by the existence of emergent gapless “photon” modes and deconfined excitations, the latter of which carry nonquantized mobile charges. We suggest that such phases may exist in the quantum regimes of various nearly ferroelectric materials. 

In devices based on two-dimensional electron gases (2DEGs), gate electrodes can be used to tune the electronic properties by controlling the electron density. Despite the prevalence of such gated systems, the properties of 2DEGs in these environments remain poorly understood quantitatively. To address this, we have studied the 2DEG in a dual-gate geometry using quantum Monte Carlo simulations alongside simpler approximate methods, and we have mapped out the phase diagram of the gated 2DEG as a function of electron density and gate distance. We find that the Wigner crystal is unstable at all densities when the gates are sufficiently close to the 2DEG, and we identify the critical gate distance at which the Wigner crystal phase appears. For larger gate separations, we determine the phase boundary for the reentrant crystal to liquid transition that occurs with decreasing density. Our Letter is particularly relevant to Wigner crystal phases recently observed in a variety of gated two-dimensional materials. 

Large Language Models (LLMs) show great promise as a powerful tool for scientific literature exploration. However, their effectiveness in providing scientifically accurate and comprehensive answers to complex questions within specialized domains remains an active area of research. Using the field of high-temperature cuprates as an exemplar, we evaluate the ability of LLM systems to understand the literature at the level of an expert. We construct an expert-curated database of 1,726 scientific papers that covers the history of the field, and a set of 67 expert-formulated questions that probe deep understanding of the literature. We then evaluate six different LLM-based systems for answering these questions, including both commercially available closed models and a custom retrieval-augmented generation (RAG) system capable of retrieving images alongside text. Experts then evaluate the answers of these systems against a rubric that assesses balanced perspectives, factual comprehensiveness, succinctness, and evidentiary support. Among the six systems, two using RAG on curated literature outperformed existing closed models across key metrics, particularly in providing comprehensive and well-supported answers. We discuss promising aspects of LLM performances as well as critical short-comings of all the models. The set of expert-formulated questions and the rubric will be valuable for assessing expert level performance of LLM based reasoning systems.