A novel crystal configuration of sandwiched S-Mo-Se structure (Janus SMoSe) at the monolayer limit has been synthesized and carefully characterized in this work. By controlled sulfurization of monolayer MoSe2 the top layer of selenium atoms are substituted by sulfur atoms while the bottom selenium layer remains intact. The peculiar structure of this new material is systematically investigated by Raman, photoluminescence and X-ray photoelectron spectroscopy and confirmed by transmission-electron microscopy and time-of-flight secondary ion mass spectrometry. Density-functional theory calculations are performed to better understand the Raman vibration modes and electronic structures of the Janus SMoSe monolayer, which are found to correlate well with corresponding experimental results. Finally, high basal plane hydrogen evolution reaction (HER) activity is discovered for the Janus monolayer and DFT calculation implies that the activity originates from the synergistic effect of the intrinsic defects and structural strain inherent in the Janus structure.

Janus Monolayer Transition Metal Dichalcogenides

Two-dimensional crystals have emerged as a class of materials that may impact future electronic technologies. Experimentally identifying and characterizing new functional two-dimensional materials is challenging, but also potentially rewarding. Here, we fabricate field-effect transistors based on few-layer black phosphorus crystals with thickness down to a few nanometres. Reliable transistor performance is achieved at room temperature in samples thinner than 7.5 nm, with drain current modulation on the order of 10(5) and well-developed current saturation in the I-V characteristics. The charge-carrier mobility is found to be thickness-dependent, with the highest values up to ∼ 1,000 cm(2) V(-1) s(-1) obtained for a thickness of ∼ 10 nm. Our results demonstrate the potential of black phosphorus thin crystals as a new two-dimensional material for applications in nanoelectronic devices.

http://absimage.aps.org/image/MAR14/MWS_MAR14-2013-005031.pdf

Black Phosphorus Field-effect Transistors

Magnetism in two-dimensional (2D) van der Waals (vdW) materials has recently emerged as one of the most promising areas in condensed matter research, with many exciting emerging properties and significant potential for applications ranging from topological magnonics to low-power spintronics, quantum computing, and optical communications. In the brief time after their discovery, 2D magnets have blossomed into a rich area for investigation, where fundamental concepts in magnetism are challenged by the behavior of spins that can develop at the single layer limit. However, much effort is still needed in multiple fronts before 2D magnets can be routinely used for practical implementations. In this comprehensive review, prominent authors with expertise in complementary fields of 2D magnetism (i.e., synthesis, device engineering, magneto-optics, imaging, transport, mechanics, spin excitations, and theory and simulations) have joined together to provide a genome of current knowledge and a guideline for future developments in 2D magnetic materials research.

/pdf/the-magnetic-genome-of-two-dimensional-van-der-waals-1hvraszg.pdf

The Magnetic Genome of Two-Dimensional van der Waals Materials

We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a materials composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully-automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm - namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10^4 or greater data points. The pre-processed, ready-to-use Matbench tasks and the Automatminer source code are open source and available online (this http URL). We encourage evaluating new materials ML algorithms on the MatBench benchmark and comparing them against the latest version of Automatminer.

/pdf/benchmarking-materials-property-prediction-methods-the-h18ju1allx.pdf

Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm

Author(s): Dunn, A; Wang, Q; Ganose, A; Dopp, D; Jain, A | Abstract: We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a material’s composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm—namely, that crystal graph methods appear to outperform traditional machine learning methods given ~104 or greater data points. We encourage evaluating materials ML algorithms on the Matbench benchmark and comparing them against the latest version of Automatminer.

/pdf/benchmarking-materials-property-prediction-methods-the-4o1tpogmsc.pdf

Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

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, hardware-accelerated, 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. Â© 2020, The Author(s).

/pdf/high-throughput-discovery-of-high-curie-point-two-4uev1hlmhy.pdf

High-throughput discovery of high Curie point two-dimensional ferromagnetic materials

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.

/pdf/scalability-assessment-of-group-iv-mono-chalcogenide-based-81zbcb5yzq.pdf

Scalability assessment of Group-IV mono-chalcogenide based tunnel FET.

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 hardware-accelerator-assisted high-throughput calculations, using a van der Waals density-functional-theory 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 open-circuit voltage (0.8 V), in agreement with experimental results, make rhenium disulfide a promising alternative anode material. Rhenium disulfide is a promising lithium ion battery material but its distorted structure makes computational modelling challenging. Here hardware-accelerator-assisted high-throughput DFT based structure searching is used to model the reversible lithiation of ReS2 including metal–sulphur bond cleavage.

/pdf/high-throughput-first-principles-calculations-based-1c0x37oufg.pdf

High-throughput first-principles-calculations based estimation of lithium ion storage in monolayer rhenium disulfide

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.

/pdf/theory-of-nonvolatile-resistive-switching-in-monolayer-12ja1aze8i.pdf

Theory of nonvolatile resistive switching in monolayer molybdenum disulfide with passive electrodes

Abstract Two-dimensional material-based transistors are being extensively investigated for CMOS (complementary metal oxide semiconductor) technology extension; nevertheless, downscaling appears to be challenging owing to high metal-semiconductor contact resistance. Here, we propose a functional group-engineered monolayer transistor architecture that takes advantage of MXenes’ natural material chemistry to offer low-resistive contacts. We design an automated, high-throughput computational pipeline that first performs hybrid density functional theory-based calculations to find 16 sets of complementary transistor configurations by screening more than 23,000 materials from an MXene database and then conducts self-consistent quantum transport calculations to simulate their current-voltage characteristics for channel lengths ranging from 10 nm to 3 nm. Performance of these devices has been found to meet the requirements of the international roadmap for devices and systems (IRDS) for several benchmark metrics (on current, power dissipation, delay, and subthreshold swing). The proposed balanced-mode, functional-engineered MXene transistors may lead to a realistic solution for the sub-decananometer technology scaling by enabling doping-free intrinsically low contact resistance. 

/pdf/high-throughput-design-of-functional-engineered-mxene-2cek1gw8.pdf

Arnab Kabiraj

Papers

High-throughput discovery of high Curie point two-dimensional ferromagnetic materials

Scalability assessment of Group-IV mono-chalcogenide based tunnel FET.

High-throughput first-principles-calculations based estimation of lithium ion storage in monolayer rhenium disulfide

Theory of nonvolatile resistive switching in monolayer molybdenum disulfide with passive electrodes

High-throughput design of functional-engineered MXene transistors with low-resistive contacts