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

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

Interacting Electrons, Spin Statistics, and Information Theory

We consider a nearly (or quasi) uniform gas of interacting electrons for which spin statistics play a crucial role. A previously developed procedure, based on the extension of the Levy–Lieb constrained search principle and Monte Carlo sampling of electron configurations in space, allows us to approximate the form of the kinetic-energy functional. For a spinless electron gas, this procedure led to a correlation term, which had the form of the Shannon entropy, but the resulting kinetic-energy functional does not satisfy the Lieb–Thirring inequality, which is rigorous and one of the most general relations regarding the kinetic energy. In this paper, we show that when the fermionic character of the electrons is included via a statistical spin approach, our procedure leads to correlation terms, which also have the form of the Shannon entropy and the resulting kinetic-energy functional does satisfy the Lieb–Thirring inequality. In this way we further strengthen the connection between Shannon entropy and electron correlation and, more generally, between information theory and quantum mechanics

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

The first horse

From an anthropological perspective, this research aims to shed light on the relationship between the human and the horse, but specifically on the relationship between an owner (first time horse owner) and his/her horse. It will also delve into how that relationship is affected by cultural aspects with respect to origin as well as the level of competency held by the owner/rider. What specific intercourses can exist to create a better bond between human and horse? What the ideas of our informants about horses' individuality and horses' mental capacities? And about what kind of relationships that are possible between human and horse? My research is conceived as an ethnographic study presenting an analysis of narrative data collected in twenty-five open-ended interviews with horse people (all owners/riders) who participate in different equestrian sports in two specific provinces of Italy – Umbria and Lombardia. What has emerged is the underestimation of the importance of the physical and mental characteristics of the horse at the beginning of the relationship. Elements that emerge as important factors can influence the positivity or negativity of the relationship. A greater consciousness of the subjectivity of the horse is needed in horse-buying process to better interact and develop a positive relationship with horses. Over time, owners/riders acquired a sense that horses are partners, subjects with minds and agency of their own

A strange myalgia

A 70-year-old man was admitted in our hospital with mild fever, pain, myalgia. His eosinophil count was high, leading to a diagnosis of hypereosinophilic syndrome. This case report gives rise to many questions regarding diagnosis and correct management of eosinophilic myopathies

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