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The introduction of new, safe, and reliable solid‐electrolyte chemistries and technologies can potentially overcome the challenges facing their liquid counterparts while widening the breadth of possible applications. Through tech‐historic evolution and rationally analyzing the transition from liquid‐based Li‐ion batteries (LIBs) to all‐solid‐state Li‐metal batteries (ASSLBs), a roadmap for the development of a successful oxide and sulfide‐based ASSLB focusing on interfacial challenges is introduced, while accounting for five parameters: energy density, power density, longterm stability, processing, and safety. First taking a strategic approach, this review dismantles the ASSLB into its three major components and discusses the most promising solid electrolytes and their most advantageous pairing options with oxide cathode materials and the Li metal anode. A thorough analysis of the chemical, electrochemical, and mechanical properties of the two most promising and investigated classes of inorganic solid electrolytes, namely oxides and sulfides, is presented. Next, the overriding challenges associated with the pairing of the solid electrolyte with oxide‐based cathodes and a Li‐metal anode, leading to limited performance for solid‐state batteries are extensively addressed and possible strategies to mitigate these issues are presented. Finally, future perspectives, guidelines, and selective interface engineering strategies toward the resolution of these challenges are analyzed and discussed.

Memristive devices are among the most prominent candidates for future computer memory storage and neuromorphic computing. Though promising, the major hurdle for their industrial fabrication is their device‐to‐device and cycle‐to‐cycle variability. These occur due to the random nature of nanoionic conductive filaments, whose rupture and formation govern device operation. Changes in filament location, shape, and chemical composition cause cycle‐to‐cycle variability. This challenge is tackled by spatially confining conductive filaments with Ni nanoparticles. Ni nanoparticles are integrated on the bottom La_0.2Sr_0.7Ti_0.9Ni_0.1O_3−δelectrode by an exsolution method, in which, at high temperatures under reducing conditions, Ni cations migrate to the perovskite surface, generating metallic nanoparticles. This fabrication method offers fine control over particle size and density and ensures strong particle anchorage in the bottom electrode, preventing movement and agglomeration. In devices based on amorphous SrTiO₃, it is demonstrated that as the exsolved Ni nanoparticle diameter increases up to≈50 nm, the ratio between the ON and OFF resistance states increases from single units to 180 and the variability of the low resistance state reaches values below 5%. Exsolution is applied for the first time to engineer solid–solid interfaces extending its realm of application to electronic devices.

Specialized hardware for neural networks requires materials with tunable symmetry, retention, and speed at low power consumption. The study proposes lithium titanates, originally developed as Li‐ion battery anode materials, as promising candidates for memristive‐based neuromorphic computing hardware. By using ex‐ and in operando spectroscopy to monitor the lithium filling and emptying of structural positions during electrochemical measurements, the study also investigates the controlled formation of a metallic phase (Li₇Ti₅O₁₂) percolating through an insulating medium (Li₄Ti₅O₁₂) with no volume changes under voltage bias, thereby controlling the spatially averaged conductivity of the film device. A theoretical model to explain the observed hysteretic switching behavior based on electrochemical nonequilibrium thermodynamics is presented, in which the metal‐insulator transition results from electrically driven phase separation of Li₄Ti₅O₁₂and Li₇Ti₅O₁₂. Ability of highly lithiated phase of Li₇Ti₅O₁₂for Deep Neural Network applications is reported, given the large retentions and symmetry, and opportunity for the low lithiated phase of Li₄Ti₅O₁₂toward Spiking Neural Network applications, due to the shorter retention and large resistance changes. The findings pave the way for lithium oxides to enable thin‐film memristive devices with adjustable symmetry and retention.