4,628 research outputs found

    A mass spectrometric and quantum chemical study of the vaporisation of lead monoxide in a flow of gaseous arsenic and antimony trioxides

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    Mass spectrometric studies of the vapours over solid lead oxide in a flow of gaseous arsenic and antimony trioxides were conducted. The following ions of the ternary oxides were detected: Pb3As2O6+, Pb3AsO4+, PbAs2O4+, PbAsO2+, PbSb2O4+, and PbSbO2+. The origin of these species produced by the ionisation and/or fragmentation of ternary gaseous oxides is discussed. The PbAs2O4 species was undoubtedly identified by the determination of the appearance energy. Presumably, the Pb3As2O6 and PbSb2O4 species also existed in the gas phase. Thermodynamic data for the ternary oxides were obtained experimentally by means of a mass spectrometric Knudsen-cell method and were confirmed by quantum chemical calculations

    The extent of NGC 6822 revealed by its C stars population

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    Using the CFH12K camera, we apply the four band photometric technique to identify 904 carbon stars in an area 28' x 42' centered on NGC 6822. A few C stars, outside of this area were also discovered with the Las Campanas Swope Telescope. The NGC 6822 C star population has an average I of 19.26 mag leading to an average absolute I magnitude of -4.70 mag, a value essentially identical to the mean magnitude obtained for the C stars in IC 1613. Contrary to stars highlighting the optical image of NGC 6822, C stars are seen at large radial distances and trace a huge slightly elliptical halo which do not coincide with the huge HI cloud surrounding NGC6822. The previously unknown stellar component of NGC 6822 has a exponential scale length of 3.0' +/- 0.1' and can be traced to five scale lengths. The C/M ratio of NGC 6822 is evaluated to br 1.0 +/- 0.2.Comment: accepted, to be published in A

    Towards Density Functional Approximations from Coupled Cluster Correlation Energy Densities

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    (Semi)local density functional approximations (DFAs) are the workhorse electronic structure methods in condensed matter theory and surface science. The correlation energy density ϵc(r) (a spatial function that yields the correlation energy Ec upon integration) is central to defining such DFAs. Unlike Ec, ϵc(r) is not uniquely defined, however. Indeed, there are infinitely many functions that integrate to the correct Ec for a given electron density ρ. The challenge for constructing useful DFAs is thus to find a suitable connection between ϵc(r) and ρ. Herein, we present a new such approach by deriving ϵc(r) directly from the coupled-cluster (CC) energy expression. The corresponding energy densities are analyzed for prototypical two-electron systems. As a proof-of-principle, we construct a semilocal functional to approximate the numerical CC correlation energy densities. Importantly, the energy densities are not simply used as reference data but guide the choice of the functional form, leading to a remarkably simple and accurate correlation functional for the helium isoelectronic series. While the resulting functional is not transferable to many-electron systems (due to a lack of same-spin correlation), these results underscore the potential of the presented approach

    Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening

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    The molecular reorganization energy λ strongly influences the charge carrier mobility of organic semiconductors and is therefore an important target for molecular design. Machine learning (ML) models generally have the potential to strongly accelerate this design process (e.g. in virtual screening studies) by providing fast and accurate estimates of molecular properties. While such models are well established for simple properties (e.g. the atomization energy), λ poses a significant challenge in this context. In this paper, we address the questions of how ML models for λ can be improved and what their benefit is in high-throughput virtual screening (HTVS) studies. We find that, while improved predictive accuracy can be obtained relative to a semiempirical baseline model, the improvement in molecular discovery is somewhat marginal. In particular, the ML enhanced screenings are more effective in identifying promising candidates but lead to a less diverse sample. We further use substructure analysis to derive a general design rule for organic molecules with low λ from the HTVS results
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