Evolution of Local Structural Motifs in Colloidal Quantum Dot Semiconductor Nanocrystals Leading to Nanofaceting.

Colloidal nanocrystals (NCs) have shown remarkable promise for optoelectronics, energy harvesting, photonics, and biomedical imaging. In addition to optimizing quantum confinement, the current challenge is to obtain a better understanding of the critical processing steps and their influence on the evolution of structural motifs. Computational simulations and electron microscopy presented in this work show that nanofaceting can occur during nanocrystal synthesis from a Pb-poor environment in a polar solvent. This could explain the curved interfaces and the olivelike-shaped NCs observed experimentally when these conditions are employed. Furthermore, the wettability of the PbS NCs solid film can be further modified via stoichiometry control, which impacts the interface band bending and, therefore, processes such as multiple junction deposition and interparticle epitaxial growth. Our results suggest that nanofaceting in NCs can become an inherent advantage when used to modulate band structures beyond what is traditionally possible in bulk crystals.

Microscopic observation of two-level systems in a metallic glass model.

The low-temperature quasi-universal behavior of amorphous solids has been attributed to the existence of spatially localized tunneling defects found in the low-energy regions of the potential energy landscape. Computational models of glasses can be studied to elucidate the microscopic nature of these defects. Recent simulation work has demonstrated the means of generating stable glassy configurations for models that mimic metallic glasses using the swap Monte Carlo algorithm. Building on these studies, we present an extensive exploration of the glassy metabasins of the potential energy landscape of a variant of the most widely used model of metallic glasses. We carefully identify tunneling defects and reveal their depletion with increased glass stability. The density of tunneling defects near the experimental glass transition temperature appears to be in good agreement with experimental measurements.

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.

The nature of non-phononic excitations in disordered systems.

The frequency scaling exponent of low-frequency excitations in microscopically small glasses, which do not allow for the existence of waves (phonons), has been in the focus of the recent literature. The density of states g(ω) of these modes obeys an ωs scaling, where the exponent s, ranging between 2 and 5, depends on the quenching protocol. The orgin of these findings remains controversal. Here we show, using heterogeneous-elasticity theory, that in a marginally-stable glass sample g(ω) follows a Debye-like scaling (s = 2), and the associated excitations (type-I) are of random-matrix type. Further, using a generalisation of the theory, we demonstrate that in more stable samples, other, (type-II) excitations prevail, which are non-irrotational oscillations, associated with local frozen-in stresses. The corresponding frequency scaling exponent s is governed by the statistics of small values of the stresses and, therefore, depends on the details of the interaction potential.

Finding defects in glasses through machine learning.

Structural defects control the kinetic, thermodynamic and mechanical properties of glasses. For instance, rare quantum tunneling two-level systems (TLS) govern the physics of glasses at very low temperature. Due to their extremely low density, it is very hard to directly identify them in computer simulations. We introduce a machine learning approach to efficiently explore the potential energy landscape of glass models and identify desired classes of defects. We focus in particular on TLS and we design an algorithm that is able to rapidly predict the quantum splitting between any two amorphous configurations produced by classical simulations. This in turn allows us to shift the computational effort towards the collection and identification of a larger number of TLS, rather than the useless characterization of non-tunneling defects which are much more abundant. Finally, we interpret our machine learning model to understand how TLS are identified and characterized, thus giving direct physical insight into their microscopic nature.

Elucidation of the Nature of Structural Relaxation in Glassy d-Sorbitol.

The nature and origin of the glass transition is one of the great unsolved problems of condensed-matter science. With the rapid increase of viscosity upon cooling the liquid near the glass-transition temperature, a range of dynamical motifs are observed, revealing the sheer complexity of interactions between the amorphous units. Yet, the causal link between those motifs and the solidification process remains unclear. Here, we apply a novel approach for exploring nontrivial interactions between structural units in d-sorbitol, a canonical example of a hydrogen-bonded organic glass, by introducing a dihedral-rearrangement-indicator analysis to shed light on relaxation processes and dynamical heterogeneity, which are known for their association with the stability of a glass. We find that both α- and ß-relaxation processes are governed by cooperative and heterogeneous changes in hydrogen-bond dynamics that can be described by spatial and dihedral-angle-rearrangement indicators. The methodology and findings are of general applicability to other glass-forming systems.

Robust In-Zn-O Thin-Film Transistors with a Bilayer Heterostructure Design and a Low-Temperature Fabrication Process Using Vacuum and Solution Deposited Layers.

We report on the design, fabrication, and characterization of heterostructure In-Zn-O (IZO) thin-film transistors (TFTs) with improved performance characteristics and robust operation. The heterostructure layer is fabricated by stacking a solution-processed IZO film on top of a buffer layer, which is deposited previously using an electron beam (e-beam) evaporator. A thin buffer layer at the dielectric interface can help to template the structure of the channel. The control of the precursors and of the solvent used during the sol-gel process can help lower the temperature needed for the sol-gel condensation reaction to proceed cleanly. This boosts the overall performance of the device with a significantly reduced subthreshold swing, a four-fold mobility increase, and a two-order of magnitude larger on/off ratio. Atomistic simulations of the a-IZO structure using molecular dynamics (both classical and ab initio) and hybrid density functional theory (DFT) calculations of the electronic structure reveal the potential atomic origin of these effects.

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.

Modeling Electrical Percolation to optimize the Electromechanical Properties of CNT/Polymer Composites in Highly Stretchable Fiber Strain Sensors.

A simulation model of electrical percolation through a three-dimensional network of curved CNTs is developed in order to analyze the electromechanical properties of a highly stretchable fiber strain sensor made of a CNT/polymer composite. Rigid-body movement of the curved CNTs within the polymer matrix is described analytically. Random arrangements of CNTs within the composite are generated by a Monte-Carlo simulation method and a union-find algorithm is utilized to investigate the network percolation. Consequently, the strain-induced resistance change curves are obtained in a wide strain range of the composite. In order to compare our model with experimental results, two CNT/polymer composite fibers were fabricated and tested as strain sensors. Their effective CNT volume fractions are estimated by comparing the experimental data with our simulation model. The results confirm that the proposed simulation model reproduces well the experimental data and is useful for predicting and optimizing the electromechanical characteristics of highly stretchable fiber strain sensors based on CNT/polymer composites.

Similarity Between Amorphous and Crystalline Phases: The Case of TiO2.

Amorphous and crystalline materials differ in their long-range structural order. On the other hand, short-range order in amorphous and crystalline materials often appears similar. Here, we use a recently introduced method for obtaining quantitative measures for structural similarity to compare crystalline and amorphous materials. We compare seven common crystalline polymorphs of TiO2, all assembled out of TiO6 or TiO7 polyhedral building blocks, to liquid and amorphous TiO2 in a quantitative two-dimensional similarity plot. We find high structural similarity between a model of amorphous TiO2, obtained by ab initio molecular-dynamics, and the B-TiO2 crystalline polymorph. The general approach presented here sheds new light on a long-standing controversy in the structural theory of amorphous solids.

Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential.

The phase-change material, Ge2Sb2Te5, is the canonical material ingredient for next-generation storage-class memory devices used in novel computing architectures, but fundamental questions remain regarding its atomic structure and physicochemical properties. Here, we introduce a machine-learning (ML)-based interatomic potential that enables large-scale atomistic simulations of liquid, amorphous, and crystalline Ge2Sb2Te5 with an unprecedented combination of speed and density functional theory (DFT) level of accuracy. Two applications exemplify the usefulness of such an ML-driven approach: we generate a 7200-atom structural model, hitherto inaccessible with DFT simulations, that affords new insight into the medium-range structural order and we create an ensemble of uncorrelated, smaller structures, for studies of their chemical bonding with statistical significance. Our work opens the way for new atomistic insights into the fascinating and chemically complex class of phase-change materials that are used in real nonvolatile memory devices.