Chemical bonding in phase-change chalcogenides.

Almost all phase-change memory materials (PCM) contain chalcogen atoms, and their chemical bonds have been denoted both as ``electron-deficient'' [sometimes referred to as ``metavalent''] and ``electron-rich'' [``hypervalent'', multicentre]. The latter involve lone-pair electrons. We have performed calculations that can discriminate unambiguously between these two classes of bond and have shown that PCM have electron-rich, 3c-4e (``hypervalent'') bonds. Plots of charge transferred between ($ET$) and shared with ($ES$) neighbouring atoms cannot on their own distinguish between ``metavalent'' and ``hypervalent'' bonds, both of which involve single-electron bonds. PCM do not exhibit ``metavalent'' bonding and are not electron-deficient; the bonding is electron-rich of the ``hypervalent'' or multicentre type. .

Understanding Defects in Amorphous Silicon with Million-Atom Simulations and Machine Learning.

The structure of amorphous silicon (a-Si) is widely thought of as a fourfold-connected random network, and yet it is defective atoms, with fewer or more than four bonds, that make it particularly interesting. Despite many attempts to explain such "dangling-bond" and "floating-bond" defects, respectively, a unified understanding is still missing. Here, we use advanced computational chemistry methods to reveal the complex structural and energetic landscape of defects in a-Si. We study an ultra-large-scale, quantum-accurate structural model containing a million atoms, and thousands of individual defects, allowing reliable defect-related statistics to be obtained. We combine structural descriptors and machine-learned atomic energies to develop a classification of the different types of defects in a-Si. The results suggest a revision of the established floating-bond model by showing that fivefold-bonded atoms in a-Si exhibit a wide range of local environments-analogous to fivefold centers in coordination chemistry. Furthermore, it is shown that fivefold (but not threefold) coordination defects tend to cluster together. Our study provides new insights into one of the most widely studied amorphous solids, and has general implications for understanding defects in disordered materials beyond silicon alone.

The role of arsenic in the operation of sulfur-based electrical threshold switches.

Arsenic is an essential dopant in conventional silicon-based semiconductors and emerging phase-change memory (PCM), yet the detailed functional mechanism is still lacking in the latter. Here, we fabricate chalcogenide-based ovonic threshold switching (OTS) selectors, which are key units for suppressing sneak currents in 3D PCM arrays, with various As concentrations. We discovered that incorporation of As into GeS brings >100 °C increase in crystallization temperature, remarkably improving the switching repeatability and prolonging the device lifetime. These benefits arise from strengthened As-S bonds and sluggish atomic migration after As incorporation, which reduces the leakage current by more than an order of magnitude and significantly suppresses the operational voltage drift, ultimately enabling a back-end-of-line-compatible OTS selector with >12 MA/cm2 on-current, ~10 ns speed, and a lifetime approaching 1010 cycles after 450 °C annealing. These findings allow the precise performance control of GeSAs-based OTS materials for high-density 3D PCM applications.

The Myth of "Metavalency" in Phase-Change Materials.

Phase-change memory materials (PCMs) have unusual properties and important applications, and recent efforts to find improved materials have focused on their bonding mechanisms. "Metavalent bonding" or "metavalency," intermediate between "metallic" and "covalent" bonding and comprising single-electron bonds, has been proposed as a fundamentally new mechanism that is relevant both here and for halide perovskite materials. However, it is shown that PCMs, which violate the octet rule, have two types of covalent bond: two-center, two-electron (2c-2e) bonds, and electron-rich, multicenter bonds (3c-4e bonds, hyperbonds) involving lone-pair electrons. The latter have bond orders less than one and are examples of the century-old concept of "partial" bonds.

Structure and Bonding in Amorphous Red Phosphorus.

Amorphous red phosphorus (a-P) is one of the remaining puzzling cases in the structural chemistry of the elements. Here, we elucidate the structure, stability, and chemical bonding in a-P from first principles, combining machine-learning and density-functional theory (DFT) methods. We show that a-P structures exist with a range of energies slightly higher than those of phosphorus nanorods, to which they are closely related, and that the stability of a-P is linked to the degree of structural relaxation and medium-range order. We thus complete the stability range of phosphorus allotropes [Angew. Chem. Int. Ed. 2014, 53, 11629] by now including the previously poorly understood amorphous phase, and we quantify the covalent and van der Waals interactions in all main phases of phosphorus. We also study the electronic densities of states, including those of hydrogenated a-P. Beyond the present study, our structural models are expected to enable wider-ranging first-principles investigations-for example, of a-P-based battery materials.

Revealing the relationship between liquid fragility and medium-range order in silicate glasses.

Despite decades of studies, the nature of the glass transition remains elusive. In particular, the sharpness of the dynamical arrest of a melt at the glass transition is captured by its fragility. Here, we reveal that fragility is governed by the medium-range order structure. Based on neutron-diffraction data for a series of aluminosilicate glasses, we propose a measurable structural parameter that features a strong inverse correlation with fragility, namely, the average medium-range distance (MRD). We use in-situ high-temperature neutron-scattering data to discuss the physical origin of this correlation. We argue that glasses exhibiting low MRD values present an excess of small network rings. Such rings are unstable and deform more readily with changes in temperature, which tends to increase fragility. These results reveal that the sharpness of the dynamical arrest experienced by a silicate glass at the glass transition is surprisingly encoded into the stability of rings in its network.

Robust Design of High-Performance Optoelectronic Chalcogenide Crystals from High-Throughput Computation.

High-performance functional materials are the cornerstones of the continuous advance of modern science and technology, but the development of new materials is still challenging. Here, we propose a robust design strategy for novel crystalline solids based on group-theory classification and high-throughput computation, as demonstrated by the successful identification of new optoelectronic semiconductors. First, by means of theoretical group analysis and composition engineering, we obtained 78 prototypical crystal structures and built a computational materials database containing 21,060 ternary chalcogenide compounds. Our high-throughput screening of the coordination characteristics, phase stability, and electronic structures provided 97 candidate semiconductors, including 93 completely new compounds. Among them, 22 crystals with excellent dynamical and thermal stability are predicted to show high photovoltaic conversion efficiency (>30%), comparable to the currently most efficient single-junction GaAs solar cell, owing to their optimal electronic properties and outstanding optical absorption. This discovery of new chalcogenide crystals offers excellent candidates for optoelectronic applications and suggests that our design strategy is a promising way to search for unknown high-performance functional materials.

Elemental electrical switch enabling phase segregation-free operation.

Nonvolatile phase-change memory has been successfully commercialized, but further density scaling below 10 nanometers requires compositionally and structurally homogeneous materials for both the memory cell and the associated vertically stacked two-terminal access switch. The selector switches are mostly amorphous-chalcogenide Ovonic threshold switches (OTSs), operating with a nonlinear current response above a threshold voltage in the amorphous state. However, they currently suffer from the chemical complexity introduced by the quaternary or even more diverse chalcogenide compositions used. We present a single-element tellurium (Te) volatile switch with a large (≥11 megaamperes per square centimeter) drive current density, ~103 ON/OFF current ratio, and faster than 20 nanosecond switching speed. The low OFF current arises from the existence of a ~0.95­electron volt Schottky barrier at the Te­electrode interface, whereas a transient, voltage pulse­induced crystal-liquid melting transition of the pure Te leads to a high ON current. Our discovery of a single-element electrical switch may help realize denser memory chips.

ChemDataExtractor 2.0: Autopopulated Ontologies for Materials Science.

The ever-growing abundance of data found in heterogeneous sources, such as scientific publications, has forced the development of automated techniques for data extraction. While in the past, in the physical sciences domain, the focus has been on the precise extraction of individual properties, attention has recently been devoted to the extraction of higher-level relationships. Here, we present a framework for an automated population of ontologies. That is, the direct extraction of a larger group of properties linked by a semantic network. We exploit data-rich sources, such as tables within documents, and present a new model concept that enables data extraction for chemical and physical properties with the ability to organize hierarchical data as nested information. Combining these capabilities with automatically generated parsers for data extraction and forward-looking interdependency resolution, we illustrate the power of our approach via the automatic extraction of a crystallographic hierarchy of information. This includes 18 interrelated submodels of nested data, extracted from an evaluation set of scientific articles, yielding an overall precision of 92.2%, across 26 different journals. Our method and associated toolkit, ChemDataExtractor 2.0, offers a key step toward the seamless integration of primary literature sources into a data-driven scientific framework.

Cerebral Microdialysate Metabolite Monitoring using Mid-infrared Spectroscopy.

The brains of patients suffering from traumatic brain-injury (TBI) undergo dynamic chemical changes in the days following the initial trauma. Accurate and timely monitoring of these changes is of paramount importance for improved patient outcome. Conventional brain-chemistry monitoring is performed off-line by collecting and manually transferring microdialysis samples to an enzymatic colorimetric bedside analyzer every hour, which detects and quantifies the molecules of interest. However, off-line, hourly monitoring means that any subhourly neurochemical changes, which may be detrimental to patients, go unseen and thus untreated. Mid-infrared (mid-IR) spectroscopy allows rapid, reagent-free, molecular fingerprinting of liquid samples, and can be easily integrated with microfluidics. We used mid-IR transmission spectroscopy to analyze glucose, lactate, and pyruvate, three relevant brain metabolites, in the extracellular brain fluid of two TBI patients, sampled via microdialysis. Detection limits of 0.5, 0.2, and 0.1 mM were achieved for pure glucose, lactate, and pyruvate, respectively, in perfusion fluid using an external cavity-quantum cascade laser (EC-QCL) system with an integrated transmission flow-cell. Microdialysates were collected hourly, then pooled (3-4 h), and measured consecutively using the standard ISCUSflex analyzer and the EC-QCL system. There was a strong correlation between the compound concentrations obtained using the conventional bedside analyzer and the acquired mid-IR absorbance spectra, where a partial-least-squares regression model was implemented to compute concentrations. This study demonstrates the potential utility of mid-IR spectroscopy for continuous, automated, reagent-free, and online monitoring of the dynamic chemical changes in TBI patients, allowing a more timely response to adverse brain metabolism and consequently improving patient outcomes.

Origins of structural and electronic transitions in disordered silicon.

Structurally disordered materials pose fundamental questions1-4, including how different disordered phases ('polyamorphs') can coexist and transform from one phase to another5-9. Amorphous silicon has been extensively studied; it forms a fourfold-coordinated, covalent network at ambient conditions and much-higher-coordinated, metallic phases under pressure10-12. However, a detailed mechanistic understanding of the structural transitions in disordered silicon has been lacking, owing to the intrinsic limitations of even the most advanced experimental and computational techniques, for example, in terms of the system sizes accessible via simulation. Here we show how atomistic machine learning models trained on accurate quantum mechanical computations can help to describe liquid-amorphous and amorphous-amorphous transitions for a system of 100,000 atoms (ten-nanometre length scale), predicting structure, stability and electronic properties. Our simulations reveal a three-step transformation sequence for amorphous silicon under increasing external pressure. First, polyamorphic low- and high-density amorphous regions are found to coexist, rather than appearing sequentially. Then, we observe a structural collapse into a distinct very-high-density amorphous (VHDA) phase. Finally, our simulations indicate the transient nature of this VHDA phase: it rapidly nucleates crystallites, ultimately leading to the formation of a polycrystalline structure, consistent with experiments13-15 but not seen in earlier simulations11,16-18. A machine learning model for the electronic density of states confirms the onset of metallicity during VHDA formation and the subsequent crystallization. These results shed light on the liquid and amorphous states of silicon, and, in a wider context, they exemplify a machine learning-driven approach to predictive materials modelling.

Multi-level phase-change memory with ultralow power consumption and resistance drift.

By controlling the amorphous-to-crystalline relative volume, chalcogenide phase-change memory materials can provide multi-level data storage (MLS), which offers great potential for high-density storage-class memory and neuro-inspired computing. However, this type of MLS system suffers from high power consumption and a severe time-dependent resistance increase ("drift") in the amorphous phase, which limits the number of attainable storage levels. Here, we report a new type of MLS system in yttrium-doped antimony telluride, utilizing reversible multi-level phase transitions between three states, i.e., amorphous, metastable cubic and stable hexagonal crystalline phases, with ultralow power consumption (0.6-4.3 pJ) and ultralow resistance drift for the lower two states (power-law exponent < 0.007). The metastable cubic phase is stabilized by yttrium, while the evident reversible cubic-to-hexagonal transition is attributed to the sequential and directional migration of Sb atoms. Finally, the decreased heat dissipation of the material and the increase in crystallinity contribute to the overall high performance. This study opens a new way to achieve advanced multi-level phase-change memory without the need for complicated manufacturing procedures or iterative programming operations.

Tunable phase transitions and high photovoltaic performance of two-dimensional In2Ge2Te6 semiconductors.

Ultrathin semiconductors with great electrical and photovoltaic performance hold tremendous promise for fundamental research and applications in next-generation electronic devices. Here, we report new 2D direct-bandgap semiconductors, namely mono- and few-layer In2Ge2Te6, with a range of desired properties from ab initio simulations. We suggest that 2D In2Ge2Te6 samples should be highly stable and can be experimentally fabricated by mechanical exfoliation. They are predicted to exhibit extraordinary optical absorption and high photovoltaic conversion efficiency (≥31.8%), comparable to the most efficient single-junction GaAs solar cell. We reveal that, thanks to the presence of van Hove singularities in the band structure, unusual quantum-phase transitions could be induced in monolayers via electrostatic doping. Furthermore, taking bilayer In2Ge2Te6 as a prototypical system, we demonstrate the application of van der Waals pressure as a promising strategy to tune the electronic and stacking property of 2D crystals. Our work creates exciting opportunities to explore various quantum phases and atomic stacking, as well as potential applications of 2D In2Ge2Te6 in future nanoelectronics.

Chemical Bonding in Chalcogenides: The Concept of Multicenter Hyperbonding.

The precise nature of chemical-bonding interactions in amorphous, and crystalline, chalcogenides is still unclear due to the complexity arising from the delocalization of bonding, and nonbonding, electrons. Although an increasing degree of electron delocalization for elements down a column of the periodic table is widely recognized, its influence on chemical-bonding interactions, and on consequent material properties, of chalcogenides has not previously been comprehensively understood from an atomistic point of view. Here, a chemical-bonding framework is provided for understanding the behavior of chalcogenides (and, in principle, other lone-pair materials) by studying prototypical telluride nonvolatile-memory, "phase-change" materials (PCMs), and related chalcogenide compounds, via density-functional-theory molecular-dynamics (DFT-MD) simulations. Identification of the presence of previously unconsidered multicenter "hyperbonding" (lone-pair-antibonding-orbital) interactions elucidates not only the origin of various material properties, and their contrast in magnitude between amorphous and crystalline phases, but also the very similar chemical-bonding nature between crystalline PCMs and one of the bonding subgroups (with the same bond length) found in amorphous PCMs, in marked contrast to existing viewpoints. The structure-property relationship established from this new bonding-interaction perspective will help in designing improved chalcogenide materials for diverse applications, based on a fundamental chemical-bonding point of view.

Disorder by design: A data-driven approach to amorphous semiconductors without total-energy functionals.

X-ray diffraction, Amorphous silicon, Multi-objective optimization, Monte Carlo methods. This paper addresses a difficult inverse problem that involves the reconstruction of a three-dimensional model of tetrahedral amorphous semiconductors via inversion of diffraction data. By posing the material-structure determination as a multiobjective optimization program, it has been shown that the problem can be solved accurately using a few structural constraints, but no total-energy functionals/forces, which describe the local chemistry of amorphous networks. The approach yields highly realistic models of amorphous silicon, with no or only a few coordination defects (≤1%), a narrow bond-angle distribution of width 9-11.5°, and an electronic gap of 0.8-1.4 eV. These data-driven information-based models have been found to produce electronic and vibrational properties of a-Si that match accurately with experimental data and rival that of the Wooten-Winer-Weaire models. The study confirms the effectiveness of a multiobjective optimization approach to the structural determination of complex materials, and resolves a long-standing dispute concerning the uniqueness of a model of tetrahedral amorphous semiconductors obtained via inversion of diffraction data.

Temperature-induced nanostructural evolution of hydrogen-rich voids in amorphous silicon: a first-principles study.

The paper presents an ab initio study of temperature-induced nanostructural evolution of hydrogen-rich voids in amorphous silicon. By using large a-Si models, obtained from classical molecular-dynamics simulations, with a realistic void-volume density of 0.2%, the dynamics of Si and H atoms on the surface of the nanometer-size cavities were studied and their effects on the shape and size of the voids were examined using first-principles density-functional simulations. The results from ab initio calculations were compared with those obtained from using the modified Stillinger-Weber potential. The temperature-induced nanostructural evolution of the voids was examined by analyzing the three-dimensional distribution of Si and H atoms on/near void surfaces using the convex-hull approximation, and computing the radius of gyration of the corresponding convex hulls. A comparison of the results with those from the simulated values of the intensity in small-angle X-ray scattering of a-Si/a-Si:H in the Guinier approximation is also provided, along with a discussion on the dynamics of bonded and non-bonded hydrogen in the vicinity of voids.

Revealing the intrinsic nature of the mid-gap defects in amorphous Ge2Sb2Te5.

Understanding the relation between the time-dependent resistance drift in the amorphous state of phase-change materials and the localised states in the band gap of the glass is crucial for the development of memory devices with increased storage density. Here a machine-learned interatomic potential is utilised to generate an ensemble of glass models of the prototypical phase-change alloy, Ge2Sb2Te5, to obtain reliable statistics. Hybrid density-functional theory is used to identify and characterise the geometric and electronic structures of the mid-gap states. 5-coordinated Ge atoms are the local defective bonding environments mainly responsible for these electronic states. The structural motif for the localisation of the mid-gap states is a crystalline-like atomic environment within the amorphous network. An extra electron is trapped spontaneously by these mid-gap states, creating deep traps in the band gap. The results provide significant insights that can help to rationalise the design of multi-level-storage memory devices.

Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon.

Amorphous materials are being described by increasingly powerful computer simulations, but new approaches are still needed to fully understand their intricate atomic structures. Here, we show how machine-learning-based techniques can give new, quantitative chemical insight into the atomic-scale structure of amorphous silicon (a-Si). We combine a quantitative description of the nearest- and next-nearest-neighbor structure with a quantitative description of local stability. The analysis is applied to an ensemble of a-Si networks in which we tailor the degree of ordering by varying the quench rates down to 1010  K s-1 . Our approach associates coordination defects in a-Si with distinct stability regions and it has also been applied to liquid Si, where it traces a clear-cut transition in local energies during vitrification. The method is straightforward and inexpensive to apply, and therefore expected to have more general significance for developing a quantitative understanding of liquid and amorphous states of matter.

Mid-IR evanescent-field fiber sensor with enhanced sensitivity for volatile organic compounds.

The increasing awareness of the harsh environmental and health risks associated with air pollution has placed volatile organic compounds (VOCs) sensor technologies in elevated demand. While the currently available VOC-monitoring technologies are either bulky and expensive, or only capable of measuring a total VOC concentration, the selective detection of VOCs in the gas-phase remains a challenge. To overcome this, a novel method and device based on mid-IR evanescent-wave fiber-optic spectroscopy, which enables enhanced detection of VOCs, is hereby proposed. This is achieved by increasing the number of analyte molecules in the proximity of the evanescent field via capillary condensation inside nano-porous microparticles coated on the fiber surface. The nano-porous structure of the coating allows the VOC analytes to rapidly diffuse into the pores and become concentrated at the surface of the fiber, thereby allowing the utilization of highly sensitive evanescent-wave spectroscopy. To ascertain the effectiveness and performance of the sensor, different VOCs are measured, and the enhanced sensitivity is analyzed using a custom-built gas cell. According to the results presented here, our VOC sensor shows a significantly increased sensitivity compared to that of an uncoated fiber.

Ultrafast Nanoscale Phase-Change Memory Enabled By Single-Pulse Conditioning.

We describe how the crystallization kinetics of a suite of phase-change systems can be controlled by using a single-shot treatment via "initial crystallization" effects. Ultrarapid and highly stable phase-change structures (with excellent characteristics), viz. conventional and sub-10 nm sized cells (400 ps switching and 368 K for 10 year data retention), stackable cells (900 ps switching and 1 × 106 cycles for similar "switching-on" voltages), and multilevel configurations (800 ps switching and resistance-drift power-law coefficients <0.11) have been demonstrated. Material measurements and thermal calculations also reveal the origin of the pretreatment-assisted increase in crystallization rates and the thermal diffusion in chalcogenide structures, respectively.