Strengthening gold-gold bonds by complexing gold clusters with noble gases

We report an unexpectedly strong and complex chemical bonding of rare-gas atoms to neutral gold clusters. The bonding features are consistently reproduced at different levels of approximation within density-functional theory and beyond: from GGA, through hybrid and double-hybrid functionals, up to renormalized second-order perturbation theory. The main finding is that the adsorption of Ar, Kr, and Xe reduces electron-electron repulsion within gold dimer, causing strengthening of the Au-Au bond. Differently from the dimer, the rare-gas adsorption effects on the gold trimer's geometry and vibrational frequencies are mainly due to electron occupation of the trimer's lowest unoccupied molecular orbital. For the trimer, the theoretical results are also consistent with far-infrared multiple photon dissociation experiments.Comment: To be published in Inorganic Chemistry Communication

A quantum reactive scattering perspective on electronic nonadiabaticity

Based on quantum reactive-scattering theory, we propose a method for studying the electronic nonadiabaticity in collision processes involving electron-ion rearrangements. We investigate the state-to-state transition probability for electron-ion rearrangements with two comparable approaches. In the first approach the information of the electron is only contained in the ground-state Born-Oppenheimer potential-energy surface, which is the starting point of common reactive-scattering calculations. In the second approach, the electron is explicitly taken into account and included in the calculations at the same level as the ions. Hence, the deviation in the results between the two approaches directly reflects the electronic nonadiabaticity during the collision process. To illustrate the method, we apply it to the well-known proton-transfer model of Shin and Metiu (one electron and three ions), generalized by us in order to allow for reactive scattering channels. It is shown that our explicit electron approach is able to capture electronic nonadiabaticity and the renormalization of the reaction barrier near the classical turning points of the potential in nuclear configuration space. In contrast, system properties near the equilibrium geometry of the asymptotic scattering channels are hardly affected by electronic nonadiabatic effects. We also present an analytical expression for the transition amplitude of the asymmetric proton-transfer model based on the direct evaluation of integrals over the involved Airy functions.Comment: 14 page

Insightful classification of crystal structures using deep learning

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

Autocatalytic and cooperatively-stabilized dissociation of water on a stepped platinum surface

Water-metal interfaces are ubiquitous and play a key role in many chemical processes, from catalysis to corrosion. Whereas water adlayers on atomically flat transition metal surfaces have been investigated in depth, little is known about the chemistry of water on stepped surfaces, commonly occurring in realistic situations. Using first-principles simulations we study the adsorption of water on a stepped platinum surface. We find that water adsorbs preferentially at the step edge, forming linear clusters or chains, stabilized by the cooperative effect of chemical bonds with the substrate and hydrogen bonds. In contrast with flat Pt, at steps water molecules dissociate forming mixed hydroxyl/water structures, through an autocatalytic mechanism promoted by hydrogen bonding. Nuclear quantum effects contribute to stabilize partially dissociated cluster and chains. Together with the recently demonstrated attitude of water chains adsorbed on stepped Pt surfaces to transfer protons via thermally activated hopping, these findings candidate these systems as viable proton wires.Comment: 19 pages, 4 figure

SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates

The lack of reliable methods for identifying descriptors - the sets of parameters capturing the underlying mechanisms of a materials property - is one of the key factors hindering efficient materials development. Here, we propose a systematic approach for discovering descriptors for materials properties, within the framework of compressed-sensing based dimensionality reduction. SISSO (sure independence screening and sparsifying operator) tackles immense and correlated features spaces, and converges to the optimal solution from a combination of features relevant to the materials' property of interest. In addition, SISSO gives stable results also with small training sets. The methodology is benchmarked with the quantitative prediction of the ground-state enthalpies of octet binary materials (using ab initio data) and applied to the showcase example of predicting the metal/insulator classification of binaries (with experimental data). Accurate, predictive models are found in both cases. For the metal-insulator classification model, the predictive capability are tested beyond the training data: It rediscovers the available pressure-induced insulator->metal transitions and it allows for the prediction of yet unknown transition candidates, ripe for experimental validation. As a step forward with respect to previous model-identification methods, SISSO can become an effective tool for automatic materials development.Comment: 11 pages, 5 figures, in press in Phys. Rev. Material

Big Data of Materials Science - Critical Role of the Descriptor

Statistical learning of materials properties or functions so far starts with a largely silent, non-challenged step: the choice of the set of descriptive parameters (termed descriptor). However, when the scientific connection between the descriptor and the actuating mechanisms is unclear, causality of the learned descriptor-property relation is uncertain. Thus, trustful prediction of new promising materials, identification of anomalies, and scientific advancement are doubtful. We analyse this issue and define requirements for a suited descriptor. For a classical example, the energy difference of zincblende/wurtzite and rocksalt semiconductors, we demonstrate how a meaningful descriptor can be found systematically.Comment: Accepted to Phys. Rev. Let

Theoretical evidence for unexpected O-rich phases at corners of MgO surfaces

Realistic oxide materials are often semiconductors, in particular at elevated temperatures, and their surfaces contain undercoordiated atoms at structural defects such as steps and corners. Using hybrid density-functional theory and ab initio atomistic thermodynamics, we investigate the interplay of bond-making, bond-breaking, and charge-carrier trapping at the corner defects at the (100) surface of a p-doped MgO in thermodynamic equilibrium with an O2 atmosphere. We show that by manipulating the coordination of surface atoms one can drastically change and even reverse the order of stability of reduced versus oxidized surface sites.Comment: 5 papges, 4 figure

Water on Pt(111): the importance of proton disorder

The structure of a water adlayer on Pt(111) surface is investigated by extensive first principle calculations. Only allowing for proton disorder the ground state energy can be found. This results from an interplay between water/metal chemical bonding and the hydrogen bonding of the water network. The resulting short O-Pt distance accounts for experimental evidences. The novelty of these results shed a new light on relevant aspects of water-metal interaction.Comment: 10 pages 4 figures (color