New Physics of the 30∘30^\circ Partial Dislocation in Silicon Revealed through {\em Ab Initio} Calculation

Based on {\em ab initio} calculation, we propose a new structure for the fundamental excitation of the reconstructed 30$^\circ$ partial dislocation in silicon. This soliton has a rare structure involving a five-fold coordinated atom near the dislocation core. The unique electronic structure of this defect is consistent with the electron spin resonance signature of the hitherto enigmatic thermally stable R center of plastically deformed silicon. We present the first {\em ab initio} determination of the free energy of the soliton, which is also in agreement with the experimental observation. This identification suggests the possibility of an experimental determination of the density of solitons, a key defect in understanding the plastic flow of the material.Comment: 6 pages, 5 postscript figure

Tailoring Graphene with Metals on Top

We study the effects of metallic doping on the electronic properties of graphene using density functional theory in the local density approximation in the presence of a local charging energy (LDA+U). The electronic properties are sensitive to whether graphene is doped with alkali or transition metals. We estimate the the charge transfer from a single layer of Potassium on top of graphene in terms of the local charging energy of the graphene sheet. The coating of graphene with a non-magnetic layer of Palladium, on the other hand, can lead to a magnetic instability in coated graphene due to the hybridization between the transition-metal and the carbon orbitals.Comment: 5 pages, 4 figure

Chemically active substitutional nitrogen impurity in carbon nanotubes

We investigate the nitrogen substitutional impurity in semiconducting zigzag and metallic armchair single-wall carbon nanotubes using ab initio density functional theory. At low concentrations (less than 1 atomic %), the defect state in a semiconducting tube becomes spatially localized and develops a flat energy level in the band gap. Such a localized state makes the impurity site chemically and electronically active. We find that if two neighboring tubes have their impurities facing one another, an intertube covalent bond forms. This finding opens an intriguing possibility for tunnel junctions, as well as the functionalization of suitably doped carbon nanotubes by selectively forming chemical bonds with ligands at the impurity site. If the intertube bond density is high enough, a highly packed bundle of interlinked single-wall nanotubes can form.Comment: 4 pages, 4 figures; major changes to the tex

Suppression of electron-electron repulsion and superconductivity in Ultra Small Carbon Nanotubes

Recently, ultra-small-diameter Single Wall Nano Tubes with diameter of $\sim 0.4 nm$ have been produced and many unusual properties were observed, such as superconductivity, leading to a transition temperature $T_c\sim 15^oK$, much larger than that observed in the bundles of larger diameter tubes. By a comparison between two different approaches, we discuss the issue whether a superconducting behavior in these carbon nanotubes can arise by a purely electronic mechanism. The first approach is based on the Luttinger Model while the second one, which emphasizes the role of the lattice and short range interaction, is developed starting from the Hubbard Hamiltonian. By using the latter model we predict a transition temperature of the same order of magnitude as the measured one.Comment: 7 pages, 3 figures, to appear in J. Phys.-Cond. Ma

Machine learning of microscopic structure-dynamics relationships in complex molecular systems

In many complex molecular systems, the macroscopic ensemble’s properties are controlled by microscopic dynamic events (or fluctuations) that are often difficult to detect via pattern-recognition approaches. Discovering the relationships between local structural environments and the dynamical events originating from them would allow unveiling microscopic-level structure-dynamics relationships fundamental to understand the macroscopic behavior of complex systems. Here we show that, by coupling advanced structural (e.g. Smooth Overlap of Atomic Positions, SOAP) with local dynamical descriptors (e.g. Local Environment and Neighbor Shuffling, LENS) in a unique dataset, it is possible to improve both individual SOAP- and LENS-based analyses, obtaining a more complete characterization of the system under study. As representative examples, we use various molecular systems with diverse internal structural dynamics. On the one hand, we demonstrate how the combination of structural and dynamical descriptors facilitates decoupling relevant dynamical fluctuations from noise, overcoming the intrinsic limits of the individual analyses. Furthermore, machine learning approaches also allow extracting from such combined structural/dynamical dataset useful microscopic-level relationships, relating key local dynamical events (e.g. LENS fluctuations) occurring in the systems to the local structural (SOAP) environments they originate from. Given its abstract nature, we believe that such an approach will be useful in revealing hidden microscopic structure-dynamics relationships fundamental to rationalize the behavior of a variety of complex systems, not necessarily limited to the atomistic and molecular scales

Cause of Death and Predictors of All-Cause Mortality in Anticoagulated Patients With Nonvalvular Atrial Fibrillation : Data From ROCKET AF

M. Kaste on työryhmän ROCKET AF Steering Comm jäsen.Background-Atrial fibrillation is associated with higher mortality. Identification of causes of death and contemporary risk factors for all-cause mortality may guide interventions. Methods and Results-In the Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared with Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation (ROCKET AF) study, patients with nonvalvular atrial fibrillation were randomized to rivaroxaban or dose-adjusted warfarin. Cox proportional hazards regression with backward elimination identified factors at randomization that were independently associated with all-cause mortality in the 14 171 participants in the intention-to-treat population. The median age was 73 years, and the mean CHADS(2) score was 3.5. Over 1.9 years of median follow-up, 1214 (8.6%) patients died. Kaplan-Meier mortality rates were 4.2% at 1 year and 8.9% at 2 years. The majority of classified deaths (1081) were cardiovascular (72%), whereas only 6% were nonhemorrhagic stroke or systemic embolism. No significant difference in all-cause mortality was observed between the rivaroxaban and warfarin arms (P=0.15). Heart failure (hazard ratio 1.51, 95% CI 1.33-1.70, P= 75 years (hazard ratio 1.69, 95% CI 1.51-1.90, P Conclusions-In a large population of patients anticoagulated for nonvalvular atrial fibrillation, approximate to 7 in 10 deaths were cardiovascular, whereasPeer reviewe

Active control of strong plasmon-exciton coupling in biomimetic pigment-polymer antenna complexes grown by surface-initiated polymerisation from gold nanostructures

Plexcitonic antenna complexes, inspired by photosynthetic light-harvesting complexes, are formed by attachment of chlorophylls (Chl) to poly(cysteine methacrylate) (PCysMA) scaffolds grown by atom-transfer radical polymerisation from gold nanostructure arrays. In these pigment-polymer antenna complexes, localised surface plasmon resonances on gold nanostructures are strongly coupled to Chl excitons, yielding hybrid light-matter states (plexcitons) that are manifested in splitting of the plasmon band. Modelling of the extinction spectra of these systems using a simple coupled oscillator model indicates that their coupling energies are up to twice as large as those measured for LHCs from plants and bacteria. Coupling energies are correlated with the exciton density in the grafted polymer layer, consistent with the collective nature of strong plasmon-exciton coupling. Steric hinderance in fully-dense PCysMA brushes limits binding of bulky chlorophylls, but the chlorophyll concentration can be increased to ~2M, exceeding that in biological light-harvesting complexes, by controlling the grafting density and polymerisation time. Moreover, synthetic plexcitonic antenna complexes display pH- and temperature-responsiveness, facilitating active control of plasmon-exciton coupling. Because of the wide range of compatible polymer chemistries and the mild reaction conditions, plexcitonic antenna complexes may offer a versatile route to programmable molecular photonic materials

Workflows for Artificial Intelligence

The efficiency and reliability of artificial intelligence (AI)-driven physics, chemistry, biophysics, materials science and engineering depends on the acquisition of sufficient, high-quality data. Due to its all-electron, full potential treatment, and its scalability to larger systems without precision limitations, FHI-aims provides accurate ab initio data from a wide range of computer simulations, such as electronic-structure calculations and molecular dynamics. To leverage the capabilities of AI models, workflows that seamlessly integrate AI tools with FHI-aims are essential. These workflows automate the acquisition of data and their use by AI. Thus, they facilitate the iterative data exchange between AI models and simulations, allowing FHI-aims to be used as a powerful AI-guided calculation engine. Also, interpretable AI models aid in analyzing the generated data. Furthermore, AI complements ab initio studies as it enables to perform simulations at larger time and length scales. In turn, also the AI models must incorporate the physics required for an accurate representation of the ab initio data. This contribution highlights workflows developed to integrate FHI-aims with AI and future challenges

Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon

Amorphous materials are being described by increasingly powerful computer simulations, but new approaches are still needed to fully understand their intricate atomic structures. Here, we show how machine-learning-based techniques can give new, quantitative chemical insight into the atomic-scale structure of amorphous silicon (a-Si). We com- bine a quantitative description of the nearest- and next-nearest- neighbor structure with a quantitative description of local stability. The analysis is applied to an ensemble of a-Si networks in which we tailor the degree of ordering by varying the quench rates down to 10^10 K/s. Our approach associates coordination defects in a-Si with distinct stability regions and it has also been applied to liquid Si, where it traces a clear-cut transition in local energies during vitrification. The method is straightforward and inexpensive to apply, and therefore expected to have more general significance for developing a quantitative understanding of liquid and amorphous states o