842 research outputs found
Kohn-Sham decomposition in real-time time-dependent density-functional theory: An efficient tool for analyzing plasmonic excitations
The real-time-propagation formulation of time-dependent density-functional
theory (RT-TDDFT) is an efficient method for modeling the optical response of
molecules and nanoparticles. Compared to the widely adopted linear-response
TDDFT approaches based on, e.g., the Casida equations, RT-TDDFT appears,
however, lacking efficient analysis methods. This applies in particular to a
decomposition of the response in the basis of the underlying single-electron
states. In this work, we overcome this limitation by developing an analysis
method for obtaining the Kohn-Sham electron-hole decomposition in RT-TDDFT. We
demonstrate the equivalence between the developed method and the Casida
approach by a benchmark on small benzene derivatives. Then, we use the method
for analyzing the plasmonic response of icosahedral silver nanoparticles up to
Ag. Based on the analysis, we conclude that in small nanoparticles
individual single-electron transitions can split the plasmon into multiple
resonances due to strong single-electron-plasmon coupling whereas in larger
nanoparticles a distinct plasmon resonance is formed.Comment: 11 pages, 3 figure
icet - A Python library for constructing and sampling alloy cluster expansions
Alloy cluster expansions (CEs) provide an accurate and computationally
efficient mapping of the potential energy surface of multi-component systems
that enables comprehensive sampling of the many-dimensional configuration
space. Here, we introduce \textsc{icet}, a flexible, extensible, and
computationally efficient software package for the construction and sampling of
CEs. \textsc{icet} is largely written in Python for easy integration in
comprehensive workflows, including first-principles calculations for the
generation of reference data and machine learning libraries for training and
validation. The package enables training using a variety of linear regression
algorithms with and without regularization, Bayesian regression, feature
selection, and cross-validation. It also provides complementary functionality
for structure enumeration and mapping as well as data management and analysis.
Potential applications are illustrated by two examples, including the
computation of the phase diagram of a prototypical metallic alloy and the
analysis of chemical ordering in an inorganic semiconductor.Comment: 10 page
A systems view of risk factors for knee osteoarthritis reveals insights into the pathogenesis of the disease.
Early detection of osteoarthritis (OA) remains a critical yet unsolved multifaceted problem. To address the multifaceted nature of OA a systems model was developed to consolidate a number of observations on the biological, mechanical and structural components of OA and identify features common to the primary risk factors for OA (aging, obesity and joint trauma) that are present prior to the development of clinical OA. This analysis supports a unified view of the pathogenesis of OA such that the risk for developing OA emerges when one of the components of the disease (e.g., mechanical) becomes abnormal, and it is the interaction with the other components (e.g., biological and/or structural) that influences the ultimate convergence to cartilage breakdown and progression to clinical OA. The model, applied in a stimulus-response format, demonstrated that a mechanical stimulus at baseline can enhance the sensitivity of a biomarker to predict cartilage thinning in a 5Â year follow-up in patients with knee OA. The systems approach provides new insight into the pathogenesis of the disease and offers the basis for developing multidisciplinary studies to address early detection and treatment at a stage in the disease where disease modification has the greatest potential for a successful outcome
Revealing the free energy landscape of halide perovskites: Metastability and transition characters in CsPbBr and MAPbI
Halide perovskites have emerged as a promising class of materials for
photovoltaic applications. A challenge in these applications is how to prevent
the crystal structure from degradation to photovoltaically inactive phases,
which requires an understanding of the free energy landscape of these
materials. Here, we uncover the free energy landscape of two prototypical
halide perovskites, CsPbBr and MAPbI via atomic scale simulations using
umbrella sampling and machine-learned potentials. For CsPbBr we find very
small free energy differences and barriers close to the transition temperatures
for both the tetragonal-to-cubic and the orthorhombic-to-tetragonal transition.
For MAPbI, however, the situation is more intricate. In particular the
orthorhombic-to-tetragonal transition exhibits a large free energy barrier and
there are several competing tetragonal phases. Using large-scale molecular
dynamics simulations we explore the character of these transition and observe
latent heat and a discrete change in structural parameters for the
tetragonal-to-cubic phase transition in both CsPbBr and MAPbI
indicating first-order transitions. We find that in MAPbI the orthorhombic
phase has an extended metastability range and furthermore identify a second
metastable tetragonal phase. Finally, we compile a phase diagram for MAPbI
that includes potential metastable phases.Comment: 9 pages, 5 figure
Measuring Air Quality for Advocacy in Africa (MA3): Feasibility and Practicality of Longitudinal Ambient PM2.5 Measurement Using Low-Cost Sensors.
Ambient air pollution in urban cities in sub-Saharan Africa (SSA) is an important public health problem with models and limited monitoring data indicating high concentrations of pollutants such as fine particulate matter (PM2.5). On most global air quality index maps, however, information about ambient pollution from SSA is scarce. We evaluated the feasibility and practicality of longitudinal measurements of ambient PM2.5 using low-cost air quality sensors (Purple Air-II-SD) across thirteen locations in seven countries in SSA. Devices were used to gather data over a 30-day period with the aim of assessing the efficiency of its data recovery rate and identifying challenges experienced by users in each location. The median data recovery rate was 94% (range: 72% to 100%). The mean 24 h concentration measured across all sites was 38 ”g/m3 with the highest PM2.5 period average concentration of 91 ”g/m3 measured in Kampala, Uganda and lowest concentrations of 15 ”g/m3 measured in Faraja, The Gambia. Kampala in Uganda and Nnewi in Nigeria recorded the longest periods with concentrations >250”g/m3. Power outages, SD memory card issues, internet connectivity problems and device safety concerns were important challenges experienced when using Purple Air-II-SD sensors. Despite some operational challenges, this study demonstrated that it is reasonably practicable and feasible to establish a network of low-cost devices to provide data on local PM2.5 concentrations in SSA countries. Such data are crucially needed to raise public, societal and policymaker awareness about air pollution across SSA
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