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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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]