14,143 research outputs found

    Hexabenzo[4.4.4]propellane:  A Helical Molecular Platform for the Construction of Electroactive Materials

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    Helical hexabenzo[4.4.4]propellane (a relative of hexaphenylethane) and its derivatives are synthesized and their structures are established by X-ray crystallography. Isolation and X-ray crystallographic characterization of a robust trication-radical salt of hexamethoxypropellane derivative confirms that its framework is stable toward oxidative (aliphatic) C−C bond cleavage. It is also demonstrated that propellane can be easily brominated at the 4,4‘-positions of the biphenyl linkages for its usage as a molecular platform for the preparation of electroactive materials

    Simultaneous Ejection of Six Electrons at a Constant Potential by Hexakis(4-ferrocenylphenyl)benzene

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    A simple synthesis of a dendritic hexaferrocenyl electron donor (5) is described in which six ferrocene moieties are connected at the vertices of the propeller of the hexaphenylbenzene core. The molecular structure of 5 is confirmed by X-ray crystallography. An electrochemical analysis along with redox titrations (which are tantamount to coulometry) confirmed that it ejects six electrons at a single potential

    Insightful classification of crystal structures using deep learning

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    Computational methods that automatically extract knowledge from data are critical for enabling data-driven materials science. A reliable identification of lattice symmetry is a crucial first step for materials characterization and analytics. Current methods require a user-specified threshold, and are unable to detect average symmetries for defective structures. Here, we propose a machine-learning-based approach to automatically classify structures by crystal symmetry. First, we represent crystals by calculating a diffraction image, then construct a deep-learning neural-network model for classification. Our approach is able to correctly classify a dataset comprising more than 100 000 simulated crystal structures, including heavily defective ones. The internal operations of the neural network are unraveled through attentive response maps, demonstrating that it uses the same landmarks a materials scientist would use, although never explicitly instructed to do so. Our study paves the way for crystal-structure recognition of - possibly noisy and incomplete - three-dimensional structural data in big-data materials science.Comment: Nature Communications, in press (2018

    AFLOW-SYM: Platform for the complete, automatic and self-consistent symmetry analysis of crystals

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    Determination of the symmetry profile of structures is a persistent challenge in materials science. Results often vary amongst standard packages, hindering autonomous materials development by requiring continuous user attention and educated guesses. Here, we present a robust procedure for evaluating the complete suite of symmetry properties, featuring various representations for the point-, factor-, space groups, site symmetries, and Wyckoff positions. The protocol determines a system-specific mapping tolerance that yields symmetry operations entirely commensurate with fundamental crystallographic principles. The self consistent tolerance characterizes the effective spatial resolution of the reported atomic positions. The approach is compared with the most used programs and is successfully validated against the space group information provided for over 54,000 entries in the Inorganic Crystal Structure Database. Subsequently, a complete symmetry analysis is applied to all 1.7++ million entries of the AFLOW data repository. The AFLOW-SYM package has been implemented in, and made available for, public use through the automated, ab-initio\textit{ab-initio} framework AFLOW.Comment: 24 pages, 6 figure

    Structure calculation, refinement and validation using CcpNmr Analysis

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    CcpNmr Analysis provides a streamlined pipeline for both NMR chemical shift assignment and structure determination of biological macromolecules. In addition, it encompasses tools to analyse the many additional experiments that make NMR such a pivotal technique for research into complex biological questions. This report describes how CcpNmr Analysis can seamlessly link together all of the tasks in the NMR structure-determination process. It details each of the stages from generating NMR restraints [distance, dihedral,hydrogen bonds and residual dipolar couplings (RDCs)],exporting these to and subsequently re-importing them from structure-calculation software (such as the programs CYANA or ARIA) and analysing and validating the results obtained from the structure calculation to, ultimately, the streamlined deposition of the completed assignments and the refined ensemble of structures into the PDBe repository. Until recently, such solution-structure determination by NMR has been quite a laborious task, requiring multiple stages and programs. However, with the new enhancements to CcpNmr Analysis described here, this process is now much more intuitive and efficient and less error-prone
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