The pervasiveness of information technologies is generating an impressive amount of data, which need to be accessed very quickly. Nonvolatile memories (NVMs) are making inroads into high‐capacity ...storage to replace hard disk drives, fuelling the expansion of the global storage memory market. As silicon‐based flash memories are approaching their fundamental limit, vertical stacking of multiple memory cell layers, innovative device concepts, and novel materials are being investigated. In this context, emerging 2D materials, such as graphene, transition metal dichalcogenides, and black phosphorous, offer a host of physical and chemical properties, which could both improve existing memory technologies and enable the next generation of low‐cost, flexible, and wearable storage devices. Herein, an overview of graphene and related 2D materials (GRMs) in different types of NVM cells is provided, including resistive random‐access, flash, magnetic and phase‐change memories. The physical and chemical mechanisms underlying the switching of GRM‐based memory devices studied in the last decade are discussed. Although at this stage most of the proof‐of‐concept devices investigated do not compete with state‐of‐the‐art devices, a number of promising technological advancements have emerged. Here, the most relevant material properties and device structures are analyzed, emphasizing opportunities and challenges toward the realization of practical NVM devices.
The outstanding properties of graphene and related 2D materials (GRMs) make them unique components for nonvolatile memories (NVMs) with enhanced characteristics to enable the next generation of low‐cost, flexible, and wearable information‐storage devices. An overview of different GRM‐based NVM cells, including resistive random‐access, flash, magnetic, and phase‐change memories, is presented, emphasizing both opportunities and challenges toward the realization of practical NVM devices by exploiting the unique properties of GRMs.
•Machine-learning interatomic potentials (MLIPs) could accurately examine the phononic properties.•MLIPs can substitute the standard DFT-based methods for the evaluation of phononic properties.•Short ...ab-initio molecular dynamics trajectories can be used to train highly accurate MLIPs.•Full computational details are provided to facilitate the practical application.
Phononic properties are commonly studied by calculating force constants using the density functional theory (DFT) simulations. Although DFT simulations offer accurate estimations of phonon dispersion relations or thermal properties, but for low-symmetry and nanoporous structures the computational cost quickly becomes very demanding. Moreover, the computational setups may yield nonphysical imaginary frequencies in the phonon dispersion curves, impeding the assessment of phononic properties and the dynamical stability of the considered system. Here, we compute phonon dispersion relations and examine the dynamical stability of a large ensemble of novel materials and compositions. We propose a fast and convenient alternative to DFT simulations which derived from machine-learning interatomic potentials passively trained over computationally efficient ab-initio molecular dynamics trajectories. Our results for diverse two-dimensional (2D) nanomaterials confirm that the proposed computational strategy can reproduce fundamental thermal properties in close agreement with those obtained via the DFT approach. The presented method offers a stable, efficient, and convenient solution for the examination of dynamical stability and exploring the phononic properties of low-symmetry and porous 2D materials.
PDF
