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.

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.

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.

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.

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.

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.

Origin of radiation tolerance in amorphous Ge2Sb2Te5 phase-change random-access memory material.

The radiation hardness of amorphous Ge2Sb2Te5 phase-change random-access memory material has been elucidated by ab initio molecular-dynamics simulations. Ionizing radiation events have been modeled to investigate their effect on the atomic and electronic structure of the glass. Investigation of the short- and medium-range order highlights a structural recovery of the amorphous network after exposure to the high-energy events modeled in this study. Analysis of the modeled glasses reveals specific structural rearrangements in the local atomic geometry of the glass, as well as an increase in the formation of large shortest-path rings. The electronic structure of the modeled system is not significantly affected by the ionizing radiation events, since negligible differences have been observed before and after irradiation. These results provide a detailed insight into the atomistic structure of amorphous Ge2Sb2Te5 after irradiation and demonstrate the radiation hardness of the glass matrix.

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.

Optical fibre-tip probes for SERS: numerical study for design considerations.

Enhancement of sub-wavelength optical fields using sub-micron plasmonic probes has found many applications in chemical, material, biological and medical sciences. The enhancement is via localised surface-plasmon resonance (LSPR) which enables the highly sensitive vibrational-spectroscopy technique of surface-enhanced Raman scattering (SERS). Combining SERS with optical fibres can allow the monitoring of biochemical reactions in situ with high resolution. Here, we study the electromagnetic-field enhancement of a tapered optical fibre-tip coated with gold nanoparticles (AuNPs) using finite-element simulations. We investigate the electric-field enhancement associated with metallic NPs and study the effect of parameters such as tip-aperture radius, cone angle, nanoparticle size and gaps between them. Our study provides an understanding of the design and application of metal-nanoparticle-coated optical-fibre-tip probes for SERS. The approach of using fibre-coupled delivery adds flexibility and simplifies the system requirements in SERS, making it suitable for cellular imaging and mapping bio-interfaces.

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures.

We demonstrate how machine-learning based interatomic potentials can be used to model guest atoms in host structures. Specifically, we generate Gaussian approximation potential (GAP) models for the interaction of lithium atoms with graphene, graphite, and disordered carbon nanostructures, based on reference density functional theory data. Rather than treating the full Li-C system, we demonstrate how the energy and force differences arising from Li intercalation can be modeled and then added to a (prexisting and unmodified) GAP model of pure elemental carbon. Furthermore, we show the benefit of using an explicit pair potential fit to capture "effective" Li-Li interactions and to improve the performance of the GAP model. This provides proof-of-concept for modeling guest atoms in host frameworks with machine-learning based potentials and in the longer run is promising for carrying out detailed atomistic studies of battery materials.

The significance of the amorphous potential energy landscape for dictating glassy dynamics and driving solid-state crystallisation.

The fundamental origins surrounding the dynamics of disordered solids near their characteristic glass transitions continue to be fiercely debated, even though a vast number of materials can form amorphous solids, including small-molecule organic, inorganic, covalent, metallic, and even large biological systems. The glass-transition temperature, Tg, can be readily detected by a diverse set of techniques, but given that these measurement modalities probe vastly different processes, there has been significant debate regarding the question of why Tg can be detected across all of them. Here we show clear experimental and computational evidence in support of a theory that proposes that the shape and structure of the potential-energy surface (PES) is the fundamental factor underlying the glass-transition processes, regardless of the frequency that experimental methods probe. Whilst this has been proposed previously, we demonstrate, using ab initio molecular-dynamics (AIMD) simulations, that it is of critical importance to carefully consider the complete PES - both the intra-molecular and inter-molecular features - in order to fully understand the entire range of atomic-dynamical processes in disordered solids. Finally, we show that it is possible to utilise this dependence to directly manipulate and harness amorphous dynamics in order to control the behaviour of such solids by using high-powered terahertz pulses to induce crystallisation and preferential crystal-polymorph growth in glasses. Combined, these findings provide compelling evidence that the PES landscape, and the corresponding energy barriers, are the ultimate controlling feature behind the atomic and molecular dynamics of disordered solids, regardless of the frequency at which they occur.

Ultrafast phase-change logic device driven by melting processes.

The ultrahigh demand for faster computers is currently tackled by traditional methods such as size scaling (for increasing the number of devices), but this is rapidly becoming almost impossible, due to physical and lithographic limitations. To boost the speed of computers without increasing the number of logic devices, one of the most feasible solutions is to increase the number of operations performed by a device, which is largely impossible to achieve using current silicon-based logic devices. Multiple operations in phase-change-based logic devices have been achieved using crystallization; however, they can achieve mostly speeds of several hundreds of nanoseconds. A difficulty also arises from the trade-off between the speed of crystallization and long-term stability of the amorphous phase. We here instead control the process of melting through premelting disordering effects, while maintaining the superior advantage of phase-change-based logic devices over silicon-based logic devices. A melting speed of just 900 ps was achieved to perform multiple Boolean algebraic operations (e.g., NOR and NOT). Ab initio molecular-dynamics simulations and in situ electrical characterization revealed the origin (i.e., bond buckling of atoms) and kinetics (e.g., discontinuouslike behavior) of melting through premelting disordering, which were key to increasing the melting speeds. By a subtle investigation of the well-characterized phase-transition behavior, this simple method provides an elegant solution to boost significantly the speed of phase-change-based in-memory logic devices, thus paving the way for achieving computers that can perform computations approaching terahertz processing rates.

Nanomechanical gas sensing with nonlinear resonant cantilevers.

Cantilevers play an important role as linear transducers in nanoscience, with nanomechanical detection of mass and stress as a clear example. We performed gas sensing experiments with a standard functionalized cantilever driven strongly into the regime of nonlinear oscillations. We compared the cantilever response to the selective adsorption of ethanol vapour in the nonlinear regime, to the ones obtained in the conventional linear static and dynamic sensing modes. In the nonlinearly driven cantilever, the adsorption and desorption-induced frequency shifts were enhanced by over a factor of three compared to resonant sensing with the same mode in the linear regime. This demonstrates a route towards gas detectors that exploit nonlinearity to enhance the responsivity, which can be implemented with standard cantilever devices.

On the analogy between photoluminescence and carrier-type reversal in Bi- and Pb-doped glasses.

Reaction order in Bi-doped oxide glasses depends on the optical basicity of the glass host. Red and NIR photoluminescence (PL) bands result from Bi(2+) and Bin clusters, respectively. Very similar centers are present in Bi- and Pb-doped oxide and chalcogenide glasses. Bi-implanted and Bi melt-doped chalcogenide glasses display new PL bands, indicating that new Bi centers are formed. Bi-related PL bands have been observed in glasses with very similar compositions to those in which carrier-type reversal has been observed, indicating that these phenomena are related to the same Bi centers, which we suggest are interstitial Bi(2+) and Bi clusters.

Interaction of metallic nanoparticles with dielectric substrates: effect of optical constants.

In this paper, we study the local-field enhancement in a system of a metallic nanoparticle placed very near to a dielectric substrate. In such systems, intense electric fields are localized in the gap between the particle and the substrate, creating a 'hot-spot' under appropriate excitation conditions. We use finite-element numerical simulations in order to study the field enhancement in this dielectric-metal system. More specifically, we show how the optical properties of the dielectric substrate (n and k) affect the plasmonic field enhancement in the nano-gap. We also analyze the degree of field confinement in the gap and discuss it in the context of utilization for surface-enhanced Raman scattering. We finally show the fields generated by real substrates and compare them to metallic ones.

Metal oxide nanoparticle mediated enhanced Raman scattering and its use in direct monitoring of interfacial chemical reactions.

Metal oxide nanoparticles (MONPs) have widespread usage across many disciplines, but monitoring molecular processes at their surfaces in situ has not been possible. Here we demonstrate that MONPs give highly enhanced (×10(4)) Raman scattering signals from molecules at the interface permitting direct monitoring of their reactions, when placed on top of flat metallic surfaces. Experiments with different metal oxide materials and molecules indicate that the enhancement is generic and operates at the single nanoparticle level. Simulations confirm that the amplification is principally electromagnetic and is a result of optical modulation of the underlying plasmonic metallic surface by MONPs, which act as scattering antennae and couple light into the confined region sandwiched by the underlying surface. Because of additional functionalities of metal oxides as magnetic, photoelectrochemical and catalytic materials, enhanced Raman scattering mediated by MONPs opens up significant opportunities in fundamental science, allowing direct tracking and understanding of application-specific transformations at such interfaces. We show a first example by monitoring the MONP-assisted photocatalytic decomposition reaction of an organic dye by individual nanoparticles.

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.

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.

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.

Study of thioflavin-T immobilized in porous silicon and the effect of different organic vapors on the fluorescence lifetime.

Steady-state and time-resolved emission techniques have been employed to study the fluorescence properties of thioflavin-T (ThT) adsorbed on oxidized porous silicon (PSi) surfaces, with an average pore size of ∼10 nm. We found that the average fluorescence decay time of ThT, when it is adsorbed on the PSi surface, is rather long, τ(av) = 1.3 ns. We attribute this relatively long emission lifetime to the effect of the immobilization of ThT on the PSi surface, which inhibit the rotation of the aniline with respect to the benzothiazole moieties of ThT. We also measured the fluorescence properties of ThT in PSi samples in equilibrium with vapors of several liquids, such as methanol, acetonitrile, and water. We found that the fluorescence intensity drops by a factor of 10, and the average decay time, measured by a time-correlated single-photon counting technique, decreases by a factor of 3. We explain these results in terms of liquid condensation of the vapors in the PSi pores, which leads to partial dissolution of the ThT molecules in the liquid pools.