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A memristor array has emerged as a potential computing hardware for artificial intelligence (AI). It has an inherent memory effect that allows information storage in the form of easily programmable electrical conductance, making it suitable for efficient data processing without shuttling of data between the processor and memory. To realize its full potential for AI applications, fine-tuning of internal device dynamics is required to implement a network system that employs dynamic functions. Here, we provide a perspective on multicationic entropy-stabilized oxides as a widely tunable materials system for memristor applications. We highlight the potential for efficient data processing in machine learning tasks enabled by the implementation of “task specific” neural networks that derive from this material tunability. 

Entropy-stabilized oxides are single-phase, multicomponent oxides that are stabilized by a large entropy of mixing, ΔS, overcoming a positive enthalpy. Due to the −TΔS term in the Gibbs' free energy, G, it can be hypothesized that entropy-stabilized oxides demonstrate a robust thermal stability. Here, we investigate the high temperature stability (1300–1700 °C) of the prototypical entropy-stabilized rocksalt oxide (MgCoNiCuZn)0.2O in air. We find that at temperatures >1300 °C, the material gradually loses Cu and Zn with increasing temperature. Cu is lost through a selective melting as a Cu-rich liquid phase is formed. Zn is sublimed from the rocksalt phase at approximately similar temperatures to those corresponding to the Cu loss, significantly below both the melting temperature of ZnO and its solubility limit in a rocksalt phase. The elemental loss progressively reduces the entropy of mixing and results in a multiphase solid upon quenching to room temperature. We posit that the high-temperature solubility of Cu and Zn is correlated providing further evidence for entropic stabilization over general solubility arguments. 

Abstract Non‐collinear antiferromagnets (AFMs) are an exciting new platform for studying intrinsic spin Hall effects (SHEs), phenomena that arise from the materials’ band structure, Berry phase curvature, and linear response to an external electric field. In contrast to conventional SHE materials, symmetry analysis of non‐collinear antiferromagnets does not forbid non‐zero longitudinal and out‐of‐plane spin currents with polarization and predicts an anisotropy with current orientation to the magnetic lattice. Here, multi‐component out‐of‐plane spin Hall conductivities are reported in L1₂‐ordered antiferromagnetic PtMn₃thin films that are uniquely generated in the non‐collinear state. The maximum spin torque efficiencies (ξ  =J_S /J_e ≈ 0.3) are significantly larger than in Pt (ξ  ≈  0.1). Additionally, the spin Hall conductivities in the non‐collinear state exhibit the predicted orientation‐dependent anisotropy, opening the possibility for new devices with selectable spin polarization. This work demonstrates symmetry control through the magnetic lattice as a pathway to tailored functionality in magnetoelectronic systems. 

Abstract Memristors have emerged as transformative devices to enable neuromorphic and in‐memory computing, where success requires the identification and development of materials that can overcome challenges in retention and device variability. Here, high‐entropy oxide composed of Zr, Hf, Nb, Ta, Mo, and W oxides is first demonstrated as a switching material for valence change memory. This multielement oxide material provides uniform distribution and higher concentration of oxygen vacancies, limiting the stochastic behavior in resistive switching. (Zr, Hf, Nb, Ta, Mo, W) high‐entropy‐oxide‐based memristors manifest the “cocktail effect,” exhibiting comparable retention with HfO₂‐ or Ta₂O₅‐based memristors while also demonstrating the gradual conductance modulation observed in WO₃‐based memristors. The electrical characterization of these high‐entropy‐oxide‐based memristors demonstrates forming‐free operation, low device and cycle variability, gradual conductance modulation, 6‐bit operation, and long retention which are promising for neuromorphic applications. 

Abstract Monolayer hexagonal boron nitride (hBN) has been widely considered a fundamental building block for 2D heterostructures and devices. However, the controlled and scalable synthesis of hBN and its 2D heterostructures has remained a daunting challenge. Here, an hBN/graphene (hBN/G) interface‐mediated growth process for the controlled synthesis of high‐quality monolayer hBN is proposed and further demonstrated. It is discovered that the in‐plane hBN/G interface can be precisely controlled, enabling the scalable epitaxy of unidirectional monolayer hBN on graphene, which exhibits a uniform moiré superlattice consistent with single‐domain hBN, aligned to the underlying graphene lattice. Furthermore, it is identified that the deep‐ultraviolet emission at 6.12 eV stems from the 1s‐exciton state of monolayer hBN with a giant renormalized direct bandgap on graphene. This work provides a viable path for the controlled synthesis of ultraclean, wafer‐scale, atomically ordered 2D quantum materials, as well as the fabrication of 2D quantum electronic and optoelectronic devices.