Databases for two-dimensional materials host numerous ferromagnetic materials without the vital information of Curie temperature since its calculation involves a manually intensive complex process. In this work, we develop a fully automated, hardwareaccelerated, dynamic-translation based computer code, which performs first principles-based computations followed by Heisenberg model-based Monte Carlo simulations to estimate the Curie temperature from the crystal structure. We employ this code to conduct a high-throughput scan of 786 materials from a database to discover 26 materials with a Curie point beyond 400 K. For rapid data mining, we further use these results to develop an end-to-end machine learning model with generalized chemical features through an exhaustive search of the model space as well as the hyperparameters. We discover a few more high Curie point materials from different sources using this data-driven model. Such material informatics, which agrees well with recent experiments, is expected to foster practical applications of two-dimensional magnetism.

Two-dimensional materials are promising candidates for lithium ion battery anodes due to their large surface to volume ratio. The distorted T′ phase of the rhenium disulfide crystal makes the study of lithium binding more complex than for other two-dimensional materials with symmetric crystal structures. Here we explore the lithium ion storage capacity of monolayer rhenium disulfide by first-principles based calculations. We employ hardwareaccelerator-assisted high-throughput calculations, using a van der Waals density-functionaltheory based 'structure search' technique, to emulate the lithiation process. Exploring 2000 structures, each containing 49 to 98 atoms, we find the most stable lithiated structures for various lithium concentrations. We then design a delithiation algorithm and apply it to those lithiated structures for the estimation of the reversible specific capacity. Despite possessing high molar mass, a reasonably high specific capacity (214.13 mAh/g) and opencircuit voltage (0.8 V), in agreement with experimental results, make rhenium disulfide a promising alternative anode material.

Selection of appropriate channel material is the key to design high performance tunnel field effect transistor (TFET), which promises to outperform the conventional metal oxide semiconductor field effect transistor (MOSFET) in ultra-low energy switching applications. Recently discovered atomically thin GeSe, a group IV mono-chalcogenide, can be a potential candidate owing to its direct electronic band gap and low carrier effective mass. In this work we employ ballistic quantum transport model to assess the intrinsic performance limit of monolayer GeSe-TFET. We first study the electronic band structure by regular and hybrid density functional theory and develop two band k · p hamiltonian for the material. We find that the complex band wraps itself within the conduction band and valence band edges and thus signifies efficient band to band tunneling mechanism. We then use the k · p hamiltonian to calculate self-consistent solution of the transport equations within the non-equilibrium Green’s function formalism and the Poisson’s equation based electrostatic potential. Keeping the OFF-current fixed at 10 pA/μm we investigate different static and dynamic performance metrics (ON current, energy and delay) under three different constant-field scaling rules: 40, 30 and 20 nm/V. Our study shows that monolayer GeSe-TFET is scalable till 8 nm while preserving ON/OFF current ratio higher than 104.

Monoelemental two-dimensional materials (borophene, silicene, etc.) are exciting candidates for electrodes in lithium-ion batteries because of their ultralight molar mass. However, these materials' lithium-ion binding mechanism can be complex as the inherited polymorphism may induce phase changes during the charge−discharge cycles. Here, we combine geneticalgorithm-based bottom-up and stochastic top-down structure searching techniques to conduct thermodynamic scrutiny of the lithiated compounds of 2D allotropes of four elements: B, Al, Si, and P. Our first-principles-based high-throughput computations unveil polymorphism-driven lithium-ion binding process and other nonidealities (e.g., bond cleavage, adsorbent phase change, and electroplating), which lacks mention in earlier works. While monolayer B (2479 mAh/g), Al (993 mAh/g), and Si (954 mAh/g) have been demonstrated here as excellent candidates for Li-ion storage, P falls short of the expectation. Our well-designed computational framework, which always searches for lithiated structures at global minima, provides convincing thermodynamical insights and realistic reversible specific-capacity values. This will expectedly open up future experimental efforts to design monoelemental two-dimensional material-based anodes with specific polymorphic structures.

Resistive-memory devices promise to revolutionize modern computer architecture eliminating the data-shuttling bottleneck between the memory and processing unit. Recent years have seen a surge of experimental demonstrations of such devices built upon two-dimensional materials based metal–insulator–metal structures. However, the fundamental mechanism of nonvolatile resistive switching has remained elusive. Here, we conduct reactive molecular dynamics simulations for a sulfur vacancy inhabited monolayer molybdenum disulfide-based device with inert electrode systems to gain insight into such phenomena. We observe that with the application of a suitable electric field, at the vacancy positions, the sulfur atom from the other plane pops and gets arrested in the plane of the molybdenum atoms. Rigorous first principles based calculations surprisingly reveal localized metallic states (virtual filament) and stronger chemical bonding for this new atomic arrangement, explaining the nonvolatile resistive switching. We further observe that localized Joule heating plays a crucial role in restoring the popped sulfur atom to its original position. The proposed theory, which delineates both unipolar and bipolar switching, may provide useful guidelines for designing high-performance resistive-memory-based computing architecture.