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Interatomic potentials for single-layer MoS₂and MoSe₂were developed by training an artificial neural network with a reference data set generated using density functional theory. 

From left to right and top to bottom, the five Ge₂H₂⁺structures are shown:trans, monobridged, butterfly, germylidene, and linear. 

Abstract The topological features of optical vortices have been opening opportunities for free-space and on-chip photonic technologies, e.g., for multiplexed optical communications and robust information transport. In a parallel but disjoint effort, polar anisotropic van der Waals nanomaterials supporting hyperbolic phonon polaritons (HP 2 s) have been leveraged to drastically boost light-matter interactions. So far HP 2 studies have been mainly focusing on the control of their amplitude and scale features. Here we report the generation and observation of mid-infrared hyperbolic polariton vortices (HP 2 Vs) associated with reconfigurable topological charges. Spiral-shaped gold disks coated with a flake of hexagonal boron nitride are exploited to tailor spin–orbit interactions and realise deeply subwavelength HP 2 Vs. The complex interplay between excitation spin, spiral geometry and HP 2 dispersion enables robust reconfigurability of the associated topological charges. Our results reveal unique opportunities to extend the application of HP 2 s into topological photonics, quantum information processing by integrating these phenomena with single-photon emitters, robust on-chip optical applications, sensing and nanoparticle manipulation. 

Abstract Since the first isolation of graphene, the importance of van der Waals (vdW) interactions has become increasingly recognized in the burgeoning field of layered materials. In this work, infrared nanoimaging techniques and theoretical modeling are used to unravel the critical role played by interfacial vdW interactions in governing the stability of violet phosphorus (VP)—a recently rediscovered wide bandgap p‐type semiconductor—when exfoliated on different substrates. It is demonstrated that vdW interactions with the underlying substrate can have a profound influence on the stability of exfoliated VP flakes and investigate how these interactions are affected by flake thickness, substrate properties (e.g., substrate hydrophilicity, surface roughness), and the exfoliation process. These findings highlight the key role played by interfacial vdW interactions in governing the stability and physical properties of layered materials, and can be used to guide substrate selection in the preparation and study of this important class of materials. 

Abstract Violet phosphorus (VP) is garnering attention for its appealing physical properties and potential applications in optoelectronics. A comprehensive investigation of the photodegradation and thermal effects of exfoliated VP on SiO₂/Si substrates is presented. The degradation rate of VP is strongly influenced by the wavelength and exposure duration of light. Light illumination of VP above the bandgap leads to faster degradation, attributed to interactions with reactive oxygen species. Power‐dependent photoluminescence (PL) measurements at low temperature (T = 4 K) show neutral exciton (X⁰) and trion (T) intensities linearly increase with excitation power, while the energy difference between peak energies decreases. The T/X⁰spectral weight ratio increases from 0.28 at 300 K to 0.69 at 4 K, suggesting enhanced T formation due to reduced phonon scattering. Temperature‐dependent Raman is used to investigate the phonon properties of VP. Tracking peak positions of 9 Raman modes with temperature, the linear first‐order temperature coefficient is obtained and found to be linear for all modes. The results provide a deeper understanding of VP's degradation behavior and implications for optoelectronic applications. 

Hydrogen-doped perovskites can be reconfigured by electrical pulses to take on all essential functions necessary for artificial intelligence hardware. 

Habituation and sensitization (nonassociative learning) are among the most fundamental forms of learning and memory behavior present in organisms that enable adaptation and learning in dynamic environments. Emulating such features of intelligence found in nature in the solid state can serve as inspiration for algorithmic simulations in artificial neural networks and potential use in neuromorphic computing. Here, we demonstrate nonassociative learning with a prototypical Mott insulator, nickel oxide (NiO), under a variety of external stimuli at and above room temperature. Similar to biological species such as Aplysia , habituation and sensitization of NiO possess time-dependent plasticity relying on both strength and time interval between stimuli. A combination of experimental approaches and first-principles calculations reveals that such learning behavior of NiO results from dynamic modulation of its defect and electronic structure. An artificial neural network model inspired by such nonassociative learning is simulated to show advantages for an unsupervised clustering task in accuracy and reducing catastrophic interference, which could help mitigate the stability–plasticity dilemma. Mott insulators can therefore serve as building blocks to examine learning behavior noted in biology and inspire new learning algorithms for artificial intelligence.