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The results are presented of a detailed combined experimental and theoretical investigation of the influence of coadsorbed electron-donating alkali atoms and the prototypical electron acceptor molecule TCNQ (7,7,8,8-tetracyanoquinodimethane) on the Ag(100) surface. Several coadsorption phases were characterised by scanning tunnelling microscopy, low energy electron diffraction, and soft-X-ray photoelectron spectroscopy. Quantitative structural data were obtained using normal incidence X-ray standing wave (NIXSW) measurements and compared with the results of density functional theory (DFT) calculations using several different methods of dispersion correction. Generally good agreement between theory and experiment was achieved for the quantitative structures, albeit with prediction of the alkali atom heights being challenging for some methods. The adsorption structures depend sensitively on the interplay of molecule-metal charge transfer and long-range dispersion forces, which are controlled by the composition ratio between alkali atoms and TCNQ. The large difference in atomic size between K and Cs has negligible effects on stability, whereas increasing the ratio of K:TCNQ from 1:4 to 1:1 leads to a weakening of molecule-metal interaction strength in favour of stronger ionic bonds within the two-dimensional alkali-organic network. A strong dependence of the work function on the alkali donor-TCNQ acceptor co-adsorption ratio is predicted., Comment: 42 pages

In part I, we presented the ring-polymer instanton with explicit friction (RPI-EF) method and showed how it can be connected to the \textit{ab initio} electronic friction formalism. This framework allows the calculation of tunneling reaction rates that incorporate the quantum nature of the nuclei and certain types of non-adiabatic effects (NAEs) present in metals. In this second part, we analyze the performance of RPI-EF on model potentials and apply it to realistic systems. For a 1D double-well model, we benchmark the method against numerically exact results obtained from multi-layer multi-configuration time-dependent Hartree calculations. We demonstrate that RPI-EF is accurate for medium and high friction strengths and less accurate for extremely low friction values. We also show quantitatively how the inclusion of NAEs lowers the cross-over temperature into the deep tunneling regime, reduces the tunneling rates, and in certain regimes, steers the quantum dynamics by modifying the tunneling pathways. As a showcase of the efficiency of this method, we present a study of hydrogen and deuterium hopping between neighboring interstitial sites in selected bulk metals. The results show that multidimensional vibrational coupling and nuclear quantum effects have a larger impact than NAEs on the tunneling rates of diffusion in metals. Together with part I, these results advance the calculations of dissipative tunneling rates from first principles.

Reactions involving adsorbates on metallic surfaces and impurities in bulk metals are ubiquitous in a wide range of technological applications. The theoretical modelling of such reactions presents a formidable challenge for theory because nuclear quantum effects (NQEs) can play a prominent role and the coupling of the atomic motion with the electrons in the metal gives rise to important non-adiabatic effects (NAEs) that alter atomic dynamics. In this work, we derive a theoretical framework that captures both NQEs and NAEs and, due to its high efficiency, can be applied to first-principles calculations of reaction rates in high-dimensional realistic systems. In more detail, we develop a method that we coin ring polymer instanton with explicit friction (RPI-EF), starting from the ring-polymer instanton formalism applied to a system-bath model. We derive general equations that incorporate the spatial and frequency dependence of the friction tensor, and then combine this method with the \textit{ab initio} electronic friction formalism for the calculation of thermal reaction rates. We show that the connection between RPI-EF and the form of the electronic friction tensor presented in this work does not require any further approximations, and it is expected to be valid as long as the approximations of both underlying theories remain valid.

We provide an overview of the design and capabilities of the near-infrared spectrograph (NIRSpec) onboard the James Webb Space Telescope. NIRSpec is designed to be capable of carrying out low-resolution ($R\!=30\!-330$) prism spectroscopy over the wavelength range $0.6-5.3\!~\mu$m and higher resolution ($R\!=500\!-1340$ or $R\!=1320\!-3600$) grating spectroscopy over $0.7-5.2\!~\mu$m, both in single-object mode employing any one of five fixed slits, or a 3.1$\times$3.2 arcsec$^2$ integral field unit, or in multiobject mode employing a novel programmable micro-shutter device covering a 3.6$\times$3.4~arcmin$^2$ field of view. The all-reflective optical chain of NIRSpec and the performance of its different components are described, and some of the trade-offs made in designing the instrument are touched upon. The faint-end spectrophotometric sensitivity expected of NIRSpec, as well as its dependency on the energetic particle environment that its two detector arrays are likely to be subjected to in orbit are also discussed., Comment: Accepted for publication in A&A. 22 pages, 19 figures

The emergence of machine learning methods in quantum chemistry provides new methods to revisit an old problem: Can the predictive accuracy of electronic structure calculations be decoupled from their numerical bottlenecks? Previous attempts to answer this question have, among other methods, given rise to semi-empirical quantum chemistry in minimal basis representation. We present an adaptation of the recently proposed SchNet for Orbitals (SchNOrb) deep convolutional neural network model [Nature Commun. 10, 5024 (2019)] for electronic wave functions in an optimised quasi-atomic minimal basis representation. For five organic molecules ranging from 5 to 13 heavy atoms, the model accurately predicts molecular orbital energies and wavefunctions and provides access to derived properties for chemical bonding analysis. Particularly for larger molecules, the model outperforms the original atomic-orbital-based SchNOrb method in terms of accuracy and scaling. We conclude by discussing the future potential of this approach in quantum chemical workflows., Comment: 15 pages, 9 figures

Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for target electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.

The adsorption structure of the molecular switch azobenzene on Ag(111) is investigated by a combination of normal incidence x-ray standing waves and dispersion-corrected density functional theory. The inclusion of non-local collective substrate response (screening) in the dispersion correction improves the description of dense monolayers of azobenzene, which exhibit a substantial torsion of the molecule. Nevertheless, for a quantitative agreement with experiment explicit consideration of the effect of vibrational mode anharmonicity on the adsorption geometry is crucial., Comment: 12 pages, 3 figures