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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. 

Lead iodide (PbI2) has gained much interest due to its direct electronic gap in the visible range and layered crystal structure. It has thereby been considered as a promising material for applications in atomically thin optoelectronic devices. In this work, we present a detailed investigation of the effect of spin–orbit coupling (SOC) that arises from the presence of heavy atoms on the electronic and optical properties of PbI2 using first-principles calculations based on density-functional theory and many-body perturbation theory. We find that SOC not only alters the bandgap but also induces the mixing of orbital characters, resulting in a significant change in the overall band structure and charge carrier effective masses. Moreover, the band orbital mixing caused by SOC results in the dramatic change in optical transition matrix elements and, correspondingly, the absorption spectrum. Our experimentally measured absorption spectra validate the calculation results and demonstrate the importance of SOC in the optical processes of PbI2. Our findings provide insights that are important for the potential use of PbI2 as a material platform for visible optoelectronic devices. 

Both two-dimensional (2D) transitional metal dichalcogenides (TMDs) and III–V semiconductors have been considered as potential platforms for quantum technology. While 2D TMDs exhibit a large exciton binding energy, and their quantum properties can be tailored via heterostructure stacking, TMD technology is currently limited by the incompatibility with existing industrial processes. Conversely, III-nitrides have been widely used in light-emitting devices and power electronics but not leveraging excitonic quantum aspects. Recent demonstrations of 2D III-nitrides have introduced exciton binding energies rivaling TMDs, promising the possibility to achieve room-temperature quantum technologies also with III-nitrides. Here, we discuss recent advancements in the synthesis and characterizations of 2D III-nitrides with a focus on 2D free-standing structures and embedded ultrathin quantum wells. We overview the main obstacles in the material synthesis, vital solutions, and the exquisite optical properties of 2D III-nitrides that enable excitonic and quantum-light emitters. 

EPW is an open-source software for ab initio calculations of electron–phonon interactions and related materials properties. The code combines density functional perturbation theory and maximally localized Wannier functions to efficiently compute electron–phonon coupling matrix elements, and to perform predictive calculations of temperature-dependent properties and phonon-assisted quantum processes in bulk solids and low-dimensional materials. Here, we report on significant developments in the code since 2016, namely: a transport module for the calculation of charge carrier mobility under electric and magnetic fields using the Boltzmann transport equation; a superconductivity module for calculations of phonon-mediated superconductors using the anisotropic multi-band Eliashberg theory; an optics module for calculations of phonon-assisted indirect transitions; a module for the calculation of small and large polarons without supercells; and a module for calculating band structure renormalization and temperature-dependent optical spectra using the special displacement method. For each capability, we outline the methodology and implementation and provide example calculations.