Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Two-dimensional transition metal dichalcogenide nanoribbons are touted as the future extreme device downscaling for advanced logic and memory devices but remain a formidable synthetic challenge. Here, we demonstrate a ledge-directed epitaxy (LDE) of dense arrays of continuous, self-aligned, monolayer and single-crystalline MoS2 nanoribbons on β-gallium (iii) oxide (β-Ga2O3) (100) substrates. LDE MoS2 nanoribbons have spatial uniformity over a long range and transport characteristics on par with those seen in exfoliated benchmarks. Prototype MoS2-nanoribbon-based field-effect transistors exhibit high on/off ratios of 108 and an averaged room temperature electron mobility of 65 cm2 V−1 s−1. The MoS2 nanoribbons can be readily transferred to arbitrary substrates while the underlying β-Ga2O3 can be reused after mechanical exfoliation. We further demonstrate LDE as a versatile epitaxy platform for the growth of p-type WSe2 nanoribbons and lateral heterostructures made of p-WSe2 and n-MoS2 nanoribbons for futuristic electronics applications. Aligned arrays of single-crystalline monolayer TMD nanoribbons with high aspect ratios, as well as their lateral heterostructures, are realized, with the growth directed by the ledges on the β-Ga2O3 substrate. This approach provides an epitaxy platform for advanced electronics applications of TMD nanoribbons.

/pdf/ledge-directed-epitaxy-of-continuously-self-aligned-single-4ioytu84s5.pdf

Ledge-directed epitaxy of continuously self-aligned single-crystalline nanoribbons of transition metal dichalcogenides

The atomic scale ordering and properties of cubic silicon carbide (β-SiC) surfaces and nanostructures are investigated by atom-resolved room and high-temperature scanning tunnelling microscopy (STM) and spectroscopy (STS), synchrotron radiation-based valence band and core level photoelectron spectroscopy (VB-PES, CL-PES) and grazing incidence x-ray diffraction (GIXRD). In this paper, we review the latest results on the atomic scale understanding of (i) the structure of β-SiC(100) surface reconstructions, (ii) temperature-induced metallic surface phase transition, (iii) one dimensional Si(C) self-organized nanostructures having unprecedented characteristics, and on (iv) nanochemistry at SiC surfaces with hydrogen. The organization of these surface reconstructions as well as the 1D nanostructures' self-organization are primarily driven by surface stress. In this paper, we address such important issues as (i) the structure of the Si-rich 3 x 2, the Si-terminated c(4 x 2), the C-terminated c(2 x 2) reconstructions of the β-SiC(100) surface, (ii) the temperature-induced reversible c(4 x 2) ⇔ 2 x 1 metallic phase transition, (iii) the formation of highly stable (up to 900°C) Si atomic and vacancy lines, (iv) the temperature-induced sp to sp 3 diamond like surface transformation, and (v) the first example of H-induced semiconductor surface metallization on the β-SiC(100) 3 x 2 surface. The results are discussed and compared to other experimental and theoretical investigations.

Atomic scale control and understanding of cubic silicon carbide surface reconstructions, nanostructures and nanochemistry : Silicon carbide

https://tandfonline.com/doi/pdf/10.1080/14686996.2017.1393633

Unconventional aspects of electronic transport in delafossite oxides.

One dimensional (1D) hybrid metal halides with core–shell quantum wire structures can display superior photoluminescence (PL) properties, e.g. high photoluminescence quantum yield and long lifetime; however, the single component white-light emissions in 1D hybrid systems composed of face-sharing octahedral chains have been only rarely reported. Herein, we report on a new 1D organic–inorganic hybrid cadmium chloride, (2cepiH)CdCl3 (2cepi = 1-(2-chloroethyl)piperidine), containing face-sharing CdCl6 octahedral chains. This incredible 1D Cd-based halide exhibited a bright white-light upon ultraviolet photoexcitation, which had an ultra-high color rendering index of 89 along with the CIE chromaticity coordinates of (0.27, 0.31). The fundamental mechanism of white-light emission was assigned to the synergistic emissions of free excitons and self-trapped excitons, as confirmed by the temperature-dependent PL spectra. To our knowledge, it is the first time that self-trapped exciton states are reported in 1D Cd-based hybrid materials with broadband white-light emission. Furthermore, both the experimental and the calculated results confirmed the semiconducting behavior of (2cepiH)CdCl3, and the band structure was determined by the inorganic frameworks. Our work provides a new idea to design novel single component white-light emitters.

Broadband white-light emission in a one-dimensional organic–inorganic hybrid cadmium chloride with face-sharing CdCl6 octahedral chains

Electronic structure calculations have become a powerful foundation for computational materials engineering. Four major factors have enabled this unprecedented evolution, namely (i) the development of density functional theory (DFT), (ii) the creation of highly efficient computer programs to solve the Kohn-Sham equations, (iii) the integration of these programs into productivityoriented computational environments, and (iv) the phenomenal increase of computing power. In this context, we describe recent applications of the Vienna Ab-initio Simulation Package (VASP) within the MedeA ® computational environment, which provides interoperability with a comprehensive range of modeling and simulation tools. The focus is on technological applications including microelectronic materials, Li-ion batteries, high-performance ceramics, silicon carbide, and Zr alloys for nuclear power generation. A discussion of current trends including high-throughput calculations concludes this article.

/pdf/computational-materials-engineering-recent-applications-of-4l8pwcl557.pdf

Computational Materials Engineering: Recent Applications of VASP in the MedeA ® Software Environment

Predicting engineering properties of materials prior to their synthesis enables the integration of their design into the overall engineering process. In this context, the present article discusses the foundation and requirements of software platforms for predicting materials properties through modeling and simulation at the electronic, atomistic, and mesoscopic levels, addressing functionality, verification, validation, robustness, ease of use, interoperability, support, and related criteria. Based on these requirements, an assessment is made of the current state revealing two critical points in the large-scale industrial deployment of atomistic modeling, namely (i) the ability to describe multicomponent systems and to compute their structural and functional properties with sufficient accuracy and (ii) the expertise needed for translating complex engineering problems into viable modeling strategies and deriving results of direct value for the engineering process. Progress with these challenges is undeniable, as illustrated here by examples from structural and functional materials including metal alloys, polymers, battery materials, and fluids. Perspectives on the evolution of modeling software platforms show the need for fundamental research to improve the predictive power of models as well as coordination and support actions to accelerate industrial deployment.

Software Platforms for Electronic/Atomistic/Mesoscopic Modeling: Status and Perspectives

The development of density functional theory and the tremendous increase of compute power in recent decades have created a framework for the incredible success of modern computational materials engineering (CME). CME has been widely adopted in the academic world and is now established as a standard tool for industrial applications. As theory and compute resources have developed, highly efficient computer codes to solve the basic equations have been implemented and successively integrated into comprehensive computational environments leading to unprecedented increases in productivity. The MedeA software of Materials Design combines a set of comprehensive productivity tools with leading computer codes such as the Vienna Ab initio Simulation Package (VASP), LAMMPS, GIBBS and the UNiversal CLuster Expansion code (UNCLE), provides interoperability at different length and time scales. In the present review, technological applications including microelectronic materials, Li-ion batteries, disordered systems, high-throughput applications and transition-metal oxides for electronics applications are described in the context of the development of CME and with reference to the MedeA environment.

https://www.mdpi.com/2079-3197/6/4/63/pdf

Unravelling the Potential of Density Functional Theory through Integrated Computational Environments: Recent Applications of the Vienna Ab Initio Simulation Package in the MedeA® Software

Materials properties are rooted in the atomic scale. Thus, an atomistic understanding of the physics and chemistry is the foundation of computational materials engineering. The MedeA computational environment provides a highly efficient platform for atomistic simulations to predict materials properties from the fundamental interactions effective at the nanoscale. Nevertheless, many interactions and processes occur at much larger time and length scales, that need to be described with microscale and macroscale models, as exemplified by the multiphase field tool MICRESS. The predictive power of these larger scale models can be greatly increased by augmenting them with atomistic simulation data. The notion of per phase-properties including their anisotropies provides e.g., the key for the determination of effective properties of multiphase materials. The key goal of the present work is to generate a common interface between atomistic and larger scale models using a data centric approach, in which the “interface” is provided by means of a standardized data structure based on the hierarchical data format HDF5. The example HDF5 file created by Schmitz et al., Sci. Technol. Adv. Mater. 17 (2016) 411, describing a three phase Al--Cu microstructure, is taken and extended to include atomistic simulation data of the Al--Cu phases, e.g., heats of formation, elastic properties, interfacial energies etc. This is pursued with special attention on using metadata to increase transparency and reproducibility of the data provided by the atomistic simulation tool MedeA.

David Reith

Papers

Computational Materials Engineering: Recent Applications of VASP in the MedeA ® Software Environment

Software Platforms for Electronic/Atomistic/Mesoscopic Modeling: Status and Perspectives

Unravelling the Potential of Density Functional Theory through Integrated Computational Environments: Recent Applications of the Vienna Ab Initio Simulation Package in the MedeA® Software

Towards Bridging the Data Exchange Gap Between Atomistic Simulation and Larger Scale Models