Materials Screening for Disorder-Controlled Chalcogenide Crystals for Phase-Change Memory Applications

Tailoring the degree of disorder in chalcogenide phase-change materials (PCMs) plays an essential role in nonvolatile memory devices and neuro-inspired computing. Upon rapid crystallization from the amorphous phase, the flagship Ge–Sb–Te PCMs form metastable rocksalt-like structures with an unconventionally high concentration of vacancies, which results in disordered crystals exhibiting Anderson-insulating transport behavior. Here, ab initio simulations and transport experiments are combined to extend these concepts to the parent compound of Ge–Sb–Te alloys, viz., binary Sb2Te3, in the metastable rocksalt-type modification. Then a systematic computational screening over a wide range of homologous, binary and ternary chalcogenides, elucidating the critical factors that affect the stability of the rocksalt structure is carried out. The findings vastly expand the family of disorder-controlled main-group chalcogenides toward many more compositions with a tunable bandgap size for demanding phase-change applications, as well as a varying strength of spin–orbit interaction for the exploration of potential topological Anderson insulators

Machine-learning of atomic-scale properties based on physical principles

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear functionals of the potential energy, such as the total energy and atomic forces. We then give a detailed account of the Smooth Overlap of Atomic Positions (SOAP) representation and kernel, showing how it arises from an abstract representation of smooth atomic densities, and how it is related to several popular density-based representations of atomic structure. We also discuss recent generalisations that allow fine control of correlations between different atomic species, prediction and fitting of tensorial properties, and also how to construct structural kernels---applicable to comparing entire molecules or periodic systems---that go beyond an additive combination of local environments

Revisiting the Local Structure in Ge-Sb-Te based Chalcogenide Superlattices.

The technological success of phase-change materials in the field of data storage and functional systems stems from their distinctive electronic and structural peculiarities on the nanoscale. Recently, superlattice structures have been demonstrated to dramatically improve the optical and electrical performances of these chalcogenide based phase-change materials. In this perspective, unravelling the atomistic structure that originates the improvements in switching time and switching energy is paramount in order to design nanoscale structures with even enhanced functional properties. This study reveals a high- resolution atomistic insight of the [GeTe/Sb2Te3] interfacial structure by means of Extended X-Ray Absorption Fine Structure spectroscopy and Transmission Electron Microscopy. Based on our results we propose a consistent novel structure for this kind of chalcogenide superlattices

Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning

Interatomic potentials: predicting phase transformations in zirconium Machine learning leads to a new interatomic potential for zirconium that can predict phase transformations. A team led by Hongxian Zong at Xi’an Jiaotong University, China, and Turab Lookman at Los Alamos National Laboratory, U.S.A, used a Gaussian-type machine learning approach to produce an interatomic potential that predicted phase transformations in zirconium. They expressed each atomic energy contribution via changes in the local atomic environment, such as bond length, shape, and volume. The resulting machine-learning potential successfully described pure zirconium’s physical properties. When used in molecular dynamics simulations, it predicted a zirconium phase diagram as a function of both temperature and pressure that agreed well with previous experiments and simulations. Developing learnt interatomic potentials in phase-transforming systems could help us better simulate complex systems

A Novel Core Genome-Encoded Superantigen Contributes to Lethality of Community-Associated MRSA Necrotizing Pneumonia

Bacterial superantigens (SAg) stimulate T-cell hyper-activation resulting in immune modulation and severe systemic illnesses such as Staphylococcus aureus toxic shock syndrome. However, all known S. aureus SAgs are encoded by mobile genetic elements and are made by only a proportion of strains. Here, we report the discovery of a novel SAg staphylococcal enterotoxin-like toxin X (SElX) encoded in the core genome of 95% of phylogenetically diverse S. aureus strains from human and animal infections, including the epidemic community-associated methicillin-resistant S. aureus (CA-MRSA) USA300 clone. SElX has a unique predicted structure characterized by a truncated SAg B-domain, but exhibits the characteristic biological activities of a SAg including Vβ-specific T-cell mitogenicity, pyrogenicity and endotoxin enhancement. In addition, SElX is expressed by clinical isolates in vitro, and during human, bovine, and ovine infections, consistent with a broad role in S. aureus infections of multiple host species. Phylogenetic analysis suggests that the selx gene was acquired horizontally by a progenitor of the S. aureus species, followed by allelic diversification by point mutation and assortative recombination resulting in at least 17 different alleles among the major pathogenic clones. Of note, SElX variants made by human- or ruminant-specific S. aureus clones demonstrated overlapping but distinct Vβ activation profiles for human and bovine lymphocytes, indicating functional diversification of SElX in different host species. Importantly, SElX made by CA-MRSA USA300 contributed to lethality in a rabbit model of necrotizing pneumonia revealing a novel virulence determinant of CA-MRSA disease pathogenesis. Taken together, we report the discovery and characterization of a unique core genome-encoded superantigen, providing new insights into the evolution of pathogenic S. aureus and the molecular basis for severe infections caused by the CA-MRSA USA300 epidemic clone

Cluster fragments in amorphous phosphorus and their evolution under pressure

Amorphous phosphorus (a-P) has long attracted interest because of its complex atomic structure, and more recently as an anode material for batteries. However, accurately describing and understanding a-P at the atomistic level remains a challenge. Here, it is shown that large-scale molecular-dynamics simulations, enabled by a machine-learning (ML)-based interatomic potential for phosphorus, can give new insights into the atomic structure of a-P and how this structure changes under pressure. The structural model so obtained contains abundant five-membered rings, as well as more complex seven- and eight-atom clusters. Changes in the simulated first sharp diffraction peak during compression and decompression indicate a hysteresis in the recovery of medium-range order. An analysis of cluster fragments, large rings, and voids suggests that moderate pressure (up to about 5 GPa) does not break the connectivity of clusters, but higher pressure does. The work provides a starting point for further computational studies of the structure and properties of a-P, and more generally it exemplifies how ML-driven modeling can accelerate the understanding of disordered functional materials

Structure and dynamics of supercooled liquid Ge2Sb2Te5 from machine‐learning‐driven simulations

Studies of supercooled liquid phase‐change materials are important for the development of phase‐change memory and neuromorphic computing devices. Here, we use a machine‐learning‐based interatomic potential for Ge2Sb2Te5 (GST) to carry out large‐scale molecular‐dynamics simulations of liquid and supercooled liquid Ge2Sb2Te5. We demonstrate a pronounced effect of the thermostat parameters on the simulation results, and we show how using a Langevin thermostat with optimized damping values can lead to excellent agreement with reference ab initio molecular dynamics (AIMD) simulations. Structural and dynamical analyses are presented, including studies of radial and angular distributions, homopolar bonds, and the temperature‐dependent diffusivity. Our work demonstrates the usefulness of ML‐driven molecular dynamics for further studies of supercooled liquid GST, with length and time scales far exceeding those that would be accessible to AIMD