3,509 research outputs found
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
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
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