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Two-dimensional transition metal dichalcogenides (TMDs) can adopt one of several possible structures, with the most common being the trigonal prismatic and octahedral symmetry phases. Since the structure determines the electronic properties, being able to predict phase-preferences of TMDs from just the knowledge of the constituent atoms is highly desired, but has remained a long-standing problem. In this study, we applied high-throughput quantum mechanical computations with machine learning algorithms to solve this old problem. Our analysis provides insights into determining physiochemical factors that dictate the phase-preference of a TMD, identifying and going beyond the attributes considered by earlier researchers in predicting crystal structures. A knowledge of these underlying physiochemical factors not only helps us to rationalize, but also to accurately predict structural preferences. We show that machine learning algorithms are powerful tools that can be used not only to find new materials with targeted properties, but also to find connections between elemental attributes and the target property/properties that were not previously obvious.

Due to an extremely diverse phase space, La1-xSrxMnO3, as with other manganites, offers a wide range of tunability and applications including colossal magnetoresistance and use as spin-polarized electrodes. Here, we study an unprecedented, exotic surface reconstruction (6 × 6) in La1-xSrxMnO3 (x = 0.3) observed via low-energy electron diffraction (LEED). Scanning tunneling microscopy (STM) shows the surface is relatively flat, with unit-cell step heights, and X-ray photoelectron spectroscopy (XPS) reveals a strong degree of Sr segregation at the surface. By combining electron diffraction and first-principles computations, we propose that the long-range surface reconstruction consists of a Sr-segregated surface with La (6 × 6) ordering. This study expands our understanding of manganite systems and underscores their ability to form interesting surface reconstructions, driven largely by cation segregation that can potentially be controlled for tuning surface ordering.

The COVID-19 pandemic has substantially strained health systems across the globe. In particular, documented disruptions to voluntary family planning and reproductive health care due to competing health priorities, service disruptions, stockouts, and lockdowns are significantly impacting reproductive, maternal, newborn, and child health. As governments and family planning programmes grapple with how best to respond to the direct and indirect effects of the pandemic on family planning and reproductive health (FP/RH), the implementation and adaptation of evidence-based practices is crucial. In this commentary, we outline applications of the High Impact Practices in Family Planning (HIPs) towards COVID-19 response efforts. The HIPs are a set of evidence-based family planning practices which reflect global expert consensus on what works in family planning programming. Drawing upon preliminary COVID-19 data, documented experiences from prior health emergencies, and recommended programme adaptations from a variety of global health partners, we outline situations where specific HIPs may assist family planning programme managers in developing context-specific and evidence-based responses to COVID-19-related impacts on FP/RH, with the ultimate goal of ensuring the accessibility, availability, and continuity of voluntary family planning services across the world.

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov.

Ordered mesoscale structures in 2D materials induced by small misorientations have opened pathways for a wide variety of novel electronic, ferroelectric, and quantum phenomena. Until now, the only mechanism to induce this periodic ordering was via mechanical rotations between the layers, with the periodicity of the resulting moir\'e pattern being directly related to twist angle. Here we report a fundamentally new mechanism for emergence of mesoscopic periodic patterns in multilayer sulfur-containing metal phosphorous trichalcogenide, MnPS$_{3}$, induced by the electron beam. The formation under the beam of periodic hexagonal patterns with several characteristic length scales, nucleation and transitions between the phases, and local dynamics are demonstrated. The associated mechanisms are attributed to the relative contraction of the layers caused by beam-induced sulphur vacancy formation with subsequent ordering and lattice parameter change. As a result, the plasmonic response of the system is locally altered, suggesting an element of control over plasmon resonances by electron beam patterning. We pose that harnessing this phenomenon provides both insight into fundamental physics of quantum materials and opens a pathway towards device applications by enabling controlled periodic potentials on the atomic scale., Comment: Electron microscopy data and analysis codes are freely available here: https://github.com/kevinroccapriore/MnPS3

We develop a probabilistic machine learning model and use it to screen for new hybrid organic-inorganic perovskites (HOIPs) with targeted electronic band gap. The data set used for this work is highly diverse, containing multiple atomic structures for each of 192 chemically distinct HOIP formulas. Therefore, any property prediction on a given formula must be associated with an irreducible "uncertainty" that comes from its unknown atomic details. As a result, dozens of new HOIP formulas with band gap falling between 1.25 and 1.50 eV were identified and validated against suitable first-principles computations. Through this demonstration we show that the probabilistic deep learning approach is robust, versatile, and can be used to properly quantify this uncertainty. In conclusion, the probabilistic standpoint and approach described herein could be widely useful for the very common and inevitable data uncertainty which is rooted at the incompleteness of information during experiments and/or computations.

Novel Coronavirus (nCoV-2019) is a highly infectious viral outbreak that has so far infected more than 110 million people worldwide. Fast viral transmission and high infection rates have severely affected the entire population, especially the old aged and comorbid individuals leaving significantly less time to find some effective treatment strategy. In these challenging times, convalescent plasma (CP) therapy came as a ray of hope to save humankind. It is a form of passive immunization that has been used to treat various infectious diseases since 1890, including the 1918 Spanish flu, 2002/03 SARS-CoV, 2009 H1N1, 2012 MERS-CoV, and 2014 Ebola outbreak. The transfusion includes administration of CP containing a high value of neutralizing antibodies against the virus in hospitalized patients. This chapter summarizes the potential outcome of CP therapy in the treatment of nCoV-2019 patients.

Pancreatic cancer has been identified as one of the deadliest malignancies because it remains asymptomatic and usually presents in the advanced stage. Tumor immune evasion is a well-known mechanism of tumorigenesis in various forms of human malignancies. Chronic inflammation via complex networking of various inflammatory cytokines in the local tissue microenvironment dysregulate the immune system and support tumor development. Pro-inflammatory mediators present in the tumor microenvironment increase the tumor burden by causing immune suppression through the generation of myeloid-derived suppressor cells and T regulatory cells. These cells, along-with myofibroblasts, create a highly immunosuppressive and resistant tumor microenvironment and are thus considered as one of the culprits for the failure of anti-cancer chemotherapies in pancreatic adenocarcinoma patients. Targeting these myeloid-derived suppressor cells using various combinatorial approaches might have the potential for abrogating the resistance and suppressive nature of the pancreatic tumor microenvironment. Therefore, there is more curiosity in studying the crosstalk of myeloid-derived suppressor cells with other immune cells during pathological conditions and the underlying mechanisms of immunosuppression in the current scenario. In this article, the possible role of myeloid-derived suppressor cells in inflammation-mediated tumor progression of pancreatic adenocarcinoma has been discussed.

Many technological applications depend on the response of materials to electric fields, but available databases of such responses are limited. Here, we explore the infrared, piezoelectric, and dielectric properties of inorganic materials by combining high-throughput density functional perturbation theory and machine learning approaches. We compute Γ-point phonons, infrared intensities, Born-effective charges, piezoelectric, and dielectric tensors for 5015 non-metallic materials in the JARVIS-DFT database. We find 3230 and 1943 materials with at least one far and mid-infrared mode, respectively. We identify 577 high-piezoelectric materials, using a threshold of 0.5 C/m2. Using a threshold of 20, we find 593 potential high-dielectric materials. Importantly, we analyze the chemistry, symmetry, dimensionality, and geometry of the materials to find features that help explain variations in our datasets. Finally, we develop high-accuracy regression models for the highest infrared frequency and maximum Born-effective charges, and classification models for maximum piezoelectric and average dielectric tensors to accelerate discovery.

Laggera tomentosa Sch. Bip. ex Oliv. et Hiern (Asteraceae), an endemic Ethiopian medicinal plant, is traditionally used to treat various ailments. Previously, the chemical constituents of the essential oil (EO) of its leaves and inflorescence were documented. However, no data about the chemical compositions of other parts of the EOs of the plant have been reported to date. Moreover, there are no previous biological activity reports on any parts of the EOs of this plant. Thus, in this study, the EOs were isolated from the stem bark and roots of this plant by hydrodistillation and analyzed using gas chromatography-mass spectrometry to identify their components. In addition, antibacterial potentials of the oils were evaluated using the disc diffusion and minimal inhibitory concentration (MIC) methods. 2,2-diphenyl-1-picrylhydrazyl (DPPH) and hydrogen peroxide methods were also employed to assess their antioxidant properties. Oxygenated monoterpenes (71.82% and 77.51%), of which 2,5-dimethoxy- p -cymene (57.28% and 64.76%) and thymol methyl ether (9.51% and 8.93%) were identified as major components in the EOs of stem bark and roots of L. tomentosa and the oils, were the most potent in the DPPH (IC50, 0.33 ± 1.10 and 0.39 ± 0.97 mg/mL) assay, respectively. Moreover, the EOs demonstrated appreciable activity towards the gram+ ( S. aureus and B. cereus ) bacteria. Among these oils, the oil of the stem bark showed the greatest activity to the gram+ (MIC = 0.625 mg/mL) bacteria. Therefore, the overall results suggested that the EOs of L. tomentosa may be a promising prospect for pharmaceutical, food, and other industrial applications.

Titanium alloys find extensive use in the aerospace and biomedical industries due to a unique combination of strength, density, and corrosion resistance. Decades of mostly experimental research has led to a large body of knowledge of the processing-microstructure-properties linkages. But much of the existing understanding of point defects that play a significant role in the mechanical properties of titanium is based on semi-empirical rules. In this work, we present the results of a detailed self-consistent first-principles study that was developed to determine formation energies of intrinsic point defects including vacancies, self-interstitials, and extrinsic point defects, such as, interstitial and substitutional impurities/dopants. We find that most elements, regardless of size, prefer substitutional positions, but highly electronegative elements, such as C, N, O, F, S, and Cl, some of which are common impurities in Ti, occupy interstitial positions. First principles, not semi-empirical rules, can systematically predict how point defects form because of impurities in titanium. A team led by Sanjeev Nayak and Rainer Herbert at the University of Connecticut, USA, used density functional theory to model the effect of more than twenty impurities in titanium, including the first twenty chemical elements as well as transition metals commonly added to titanium. Electronegative elements such as carbon and oxygen were stable at octahedral interstitial positions in the titanium lattice, in an identical manner to interstitial titanium. In contrast, metallic impurities such as vanadium preferred substitutional sites. Finally, vacancies in the lattice only persisted if they were far enough from their interstitial titanium atom. Research into point defect formation and stability may help us control materials for advanced manufacturing processes such as 3D printing.

Purpose National wealth, income inequalities, and expenditures on education can profoundly influence the health of a nation's women, children, and adolescents. We explored the association of trends in national socioeconomic status (SES) indicators with trends in adolescent birth rates (ABRs), by nation and region. Methods An ecologic research design was employed using national-level data from the World Bank on birth rates per 1,000 women aged 15–19 years, national wealth (per capita gross domestic product or GDP), income inequality (Gini index), and expenditures on education as a percentage of GDP (EduExp). Data were available for 142 countries and seven regions for 1990–2012. Multiple linear regression for repeated measures with generalized estimating equations was used to examine independent associations. Results ABRs in 2012 varied >200-fold—with the highest rates in Sub-Saharan Africa and lowest rates in the Western Europe/Central Asia region. The median national ABR fell 40% from 72.4/1,000 in 1990 to 43.6/1,000 in 2012. The largest regional declines in ABR occurred in South Asia (70%), Europe/Central Asia (63%), and the Middle East/North Africa (53%)—regions with lower income inequality. In multivariable analyses considering change over time, ABRs were negatively associated with GDP and EduExp and positively associated with greater income inequality. Conclusions ABRs have declined globally since 1990. Declines closely followed rising socioeconomic status and were greater where income inequalities were lower in 1990. Reducing poverty and income inequalities and increasing investments in education should be essential components of national policies to prevent adolescent childbearing.

We disclose a novel, heterogeneous catalytic approach for selective coupling of C1 of N-aryltetrahydroisoquinolines with C3 of indoles in the presence of mesoporous manganese oxides. Our work involves a detailed mechanistic investigation of the reaction on the catalyst surface, backed by DFT computational studies, to understand the superior catalytic activity of manganese oxides.

An intelligent model is developed to control the water level in boiler drum. There are three different types of boiler control such as i Single element boiler drum level control, ii Two element boiler drum level control and iii Three element boiler drum level control. This paper provides the knowledge about the Fuzzy PID Controller and the various PID controller design methods such as Zeigler Nichols method, Tyreus Luyben method, Internal Model Control IMC . Comparative study is made on the performance of the PID and Fuzzy PID controller for better control system design. Sumit Patel | Vinit Sharma "Analysis of Different Controllers used for Boiler Drum Level Control by using LabVIEW Simulation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29704.pdf

The model reactions CH3 X + (NH-CH=O)M ➔ CH3 -NH-NH═O or NH═CH-O-CH3 + MX (M = none, Li, Na, K, Ag, Cu; X = F, Cl, Br) are investigated to demonstrate the feasibility of Marcus theory and the hard and soft acids and bases (HSAB) principle in predicting the reactivity of ambident nucleophiles. The delocalization indices (DI) are defined in the framework of the quantum theory of atoms in molecules (QT-AIM), and are used as the scale of softness in the HSAB principle. To react with the ambident nucleophile NH═CH-O- , the carbocation H3 C+ from CH3 X (F, Cl, Br) is actually a borderline acid according to the DI values of the forming C…N and C…O bonds in the transition states (between 0.25 and 0.49), while the counter ions are divided into three groups according to the DI values of weak interactions involving M (M…X, M…N, and M…O): group I (M = none, and Me4 N) basically show zero DI values; group II species (M = Li, Na, and K) have noticeable DI values but the magnitudes are usually less than 0.15; and group III species (M = Ag and Cu(I)) have significant DI values (0.30-0.61). On a relative basis, H3 C+ is a soft acid with respect to group I and group II counter ions, and a hard acid with respect to group III counter ions. Therefore, N-regioselectivity is found in the presence of group I and group II counter ions (M = Me4 N, Li, Na, K), while O-regioselectivity is observed in the presence of the group III counter ions (M = Ag, and Cu(I)). The hardness of atoms, groups, and molecules is also calculated with new functions that depend on ionization potential (I) and electron affinity (A) and use the atomic charges obtained from localization indices (LI), so that the regioselectivity is explained by the atomic hardness of reactive nitrogen atoms in the transition states according to the maximum hardness principle (MHP). The exact Marcus equation is derived from the simple harmonic potential energy parabola, so that the concepts of activation free energy, intrinsic activation barrier, and reaction energy are completely connected. The required intrinsic activation barriers can be either estimated from ab initio calculations on reactant, transition state, and product of the model reactions, or calculated from identity reactions. The counter ions stabilize the reactant through bridging N- and O-site of reactant of identity reactions, so that the intrinsic barriers for the salts are higher than those for free ambident anions, which is explained by the increased reorganization parameter Δr. The proper application of Marcus theory should quantitatively consider all three terms of Marcus equation, and reliably represent the results with potential energy parabolas for reactants and all products. For the model reactions, both Marcus theory and HSAB principle/MHP principle predict the N-regioselectivity when M = none, Me4 N, Li, Na, K, and the O-regioselectivity when M = Ag and Cu(I). © 2019 Wiley Periodicals, Inc.

Increasing evidence suggests a role of inflammation during the pathogenesis of osteoarthritis (OA). The local and systemic inflammation was studied in 33 patients of different KL grades, grade2 (n = 11), grade3 (n = 6) and grade4 (n = 16). The levels of cytokines, adipokines and matrix metalloproteinases (MMPs) were measured in serum and synovial fluid (SF) by flow cytometry and ELISA, respectively. The frequency of T cells and CD161 expression was measured by flow cytometry. The levels of IL-1β, IL-6 and IL-10 were significantly higher in sera and SF of patients with OA as compared to healthy control's serum. Higher levels of MMP9 and leptin and lower levels of adiponectin were observed in SF as compared to serum. The MMP9 in SF and MMP13 levels in serum and SF decreased in KL grade 4 cases. In these patients, higher levels of leptin and lower levels of adiponectin were observed in SF versus patients of lower grades. There was increased infiltration of CD8+ T cells in SF of OA cases with decreased frequency in grade 4 cases. The expression of CD161 on T cells was significantly higher in SF than peripheral blood with significant upregulation in grade 4 patients. The CD161 expression had significant positive correlation with IL-17 in the serum of patients. The ROC curves of CD161 expression significantly distinguished grade 2 and grade 4 patients. Collectively, an elevated CD161 expression on T cells in circulation and synovial compartment clearly distinguished lower and higher grade patients warranting studies to assess its role as a contributing factor towards OA progression.

Complex behavior poses challenges in extracting models from experiment. An example is spin liquid formation in frustrated magnets like Dy$_2$Ti$_2$O$_7$. Understanding has been hindered by issues including disorder, glass formation, and interpretation of scattering data. Here, we use a novel automated capability to extract model Hamiltonians from data, and to identify different magnetic regimes. This involves training an autoencoder to learn a compressed representation of three-dimensional diffuse scattering, over a wide range of spin Hamiltonians. The autoencoder finds optimal matches according to scattering and heat capacity data and provides confidence intervals. Validation tests indicate that our optimal Hamiltonian accurately predicts temperature and field dependence of both magnetic structure and magnetization, as well as glass formation and irreversibility in Dy$_2$Ti$_2$O$_7$. The autoencoder can also categorize different magnetic behaviors and eliminate background noise and artifacts in raw data. Our methodology is readily applicable to other materials and types of scattering problems., Comment: 18 pages, 6 figures

The mechanisms of phase transitions have been previously explored at various theoretical and experimental levels. For a wide variety of compounds, the majority of studies are limited by observations at fixed temperature and composition, in which case, relevant information can be determined only from the behaviors at topological and structural defects. All analyses to date utilize macroscopic descriptors derived from structural information such as polarization or octahedral tilts extracted from the atomic positions, ignoring the multiple degrees of freedom observable from atomically resolved images. In this article, we provide a solution, by exploring the mechanisms of a phase transition between the trigonal prismatic and distorted octahedral phases of layered chalcogenides in the 2D MoS2–ReS2 system from the observations of local degrees of freedom, namely atomic positions by scanning transmission electron microscopy. We employ local crystallographic analysis based on statistical learning of atomic environments to build a picture of the transition from the atomic level up and determine local and global variables controlling the local symmetry breaking. We highlight how the dependence of the average symmetry-breaking distortion amplitude on global and local concentration can be used to separate local chemical as well as global electronic effects on the transition. This approach allows for the exploring of atomic mechanisms beyond the traditional macroscopic descriptions, utilizing the imaging of compositional fluctuations in solids to explore phase transitions over a range of observed local stoichiometries and atomic configurations.

Particle sizes play a major role to mediate charge transfer, both between identical and different material surfaces. The study probes into the probable mechanism that actuates opposite polarities between two different size fractions of the same material by analyzing the charge transfer patterns of two different sizes of microcrystalline cellulose (MCC). Quantum scale calculations confirmed alteration of charge transfer capacities due to variation of moisture content predicted by multiple surface and bulk analytical techniques. Discrete Element Method (DEM) based multi-scale computational models pertinent to predict charge transfer capacities were further implemented, and the results were in accordance to the experimental charge profiles.

Herein we introduce an environmentally friendly approach to the synthesis of symmetrical and asymmetrical aromatic azo compounds by using air as the sole oxidant under mild reaction conditions in the presence of cost-effective and reusable mesoporous manganese oxide materials.

In this work, HoCrO3 and Fe substituted HoCrO3 and DyCrO3 (i.e. HoCr0.7Fe0.3O3 and DyCr0.7Fe0.3O3) powder samples were synthesized via a solution route. The structural properties of the samples were examined by Raman spectroscopy and X-ray diffraction techniques, which were further confirmed using the first-principle calculations. The dc magnetic measurements indicate that the Cr3+ ordering temperatures for the HoCrO3, HoCr0.7Fe0.3O3, and DyCr0.7Fe0.3O3 samples were 140 K, 174 K, and 160 K, respectively. The ac magnetic measurements not only confirmed the Cr3+ ordering transitions in these samples (obtained using dc magnetic measurements), but also clearly showed the Ho3+ ordering at ∼10 K in the present HoCrO3 and HoCr0.7Fe0.3O3 samples, which to our knowledge, is the first ac magnetic evidence of Ho3+ ordering in this system. The effective magnetic moments were determined to be 11.67μB, 11.30μB, and 11.27μB for the HoCrO3, HoCr0.7Fe0.3O3, and DyCr0.7Fe0.3O3 samples, respectively. For the first time, the magnetocaloric properties of HoCrO3 and HoCr0.7Fe0.3O3 were studied here, showing their potential for applications in magnetic refrigeration. In an applied dc magnetic field of 7 T, the maximum values of magnetic entropy change were determined to be 7.2 (at 20 K), 6.83 (at 20 K), and 13.08 J kg−1 K−1 (at 5 K) and the relative cooling power were 408, 387, and 500 J kg−1 for the HoCrO3, HoCr0.7Fe0.3O3, and DyCr0.7Fe0.3O3 samples, respectively.

Perovskite oxides are a promising material platform for use in a wide range of technological applications including electronics, sensors, fuel cells, and catalysis. This is owing to the extraordinary tunability of their physical and chemical properties via defect engineering. The feasibility and the stability of a defect, such as a substitutional dopant, in the host lattice is usually obtained via experiments and/or through detailed quantum mechanical calculations. Both of these conventional routes are expensive and time consuming. An alternative is a data-driven machine learning (ML)-based approach. In this work, we have applied ML techniques to identify the factors that influence defect formation energy, which is an important measure of the stability of the defects, in perovskite oxides. Using 13 elemental properties as features and random forest regression, we demonstrate a systematic approach to down-selecting from the larger set of features to those that are important, establishing a framework for accurate predictions of the defect formation energy. We quantitatively show that the most important factors that control the dopant stability are the dopant ionic size, heat of formation, effective tolerance factor, and oxidation state. Our work reveals previously unknown correlations, chemical trends, and the interplay between stability and underlying chemistries. Hence, these results showcase the efficacy of ML tools in identifying and quantifying different feature-dependencies and provide a promising route toward dopant selection in the perovskites. We have developed a framework that itself is general and can be potentially applied to other material classes.

The surface cation chemistry in (La, A)MnO3 (A = Ca, Sr and Ba) is investigated using first-principles thermodynamics. We find that, all three dopants tend to segregate to the surface over a wide range of T– $$ p_{{{\text{O}}_{2} }} $$ conditions and the tendency for segregation increases with the increase in the dopant cationic size. Moving toward the low oxygen pressure, dopants prefer to remain in the surface regions accompanied by the appropriate number of charge compensating oxygen vacancies. The situation when dopants remain in the bulk regions tends to occur close to the thermodynamic conditions that also favor the decomposition of LaMnO3. The present work serves as an important step toward understanding of factors governing the cationic surface segregation in doped LaMnO3 and opens a pathway to study other important chemical environments (such as water- and CO2-containing air) which are crucially given the fact that the ‘real-world’ air enhances cationic segregation.

Metal sulfate formation is one of the major challenges to the emissions aftertreatment catalysts. Unlike the incredibly sulfation prone nature of Pd to form PdSO$_4$, no experimental evidence exits for the PtSO$_4$ formation. Given the mystery of nonexistence of the PtSO$_4$, we explore the PtSO$_4$ using a combined approach of evolutionary algorithm based search technique and quantum mechanical computations. Experimentally known PdSO$_4$ is considered for the comparison and validation of our results. We predict many possible low-energy phases of the PtSO$_4$ and PdSO$_4$ at 0K, which are further investigated under wide range of temperature-pressure conditions. An entirely new low-energy (tetragonal $P4_2/m$) structure of the PtSO$_4$ and PdSO$_4$ is predicted, which appears to be the most stable phase of the PtSO$_4$ and a competing phase of the experimentally known monoclinic $C_2/c$ phase of PdSO$_4$. Phase stability at finite temperature is further examined and verified by free energy calculations of sulfates towards their possible decomposition products. Finally, temperature-pressure phase diagrams are computationally established for both PtSO$_4$ and PdSO$_4$.

An ab initio study of structural, electronic, elastic, and dielectric properties of KMgF3 in cubic perovskite structure is presented in the framework of density functional theory. The calculations presented here employ generalized gradient approximation with projector augmented wave method. The fully relaxed structural parameters are found to be in reasonable agreement with available experimental data and with previous theoretical work. The independent elastic constants of cubic KMgF3 are derived from the derivative of total energy as a function of lattice strain in full detail. The bulk modulus and its first pressure derivative are obtained by fitting total energy versus volume data to a Murnaghan equation of state. The electronic band structure, total density of states, and projected density of states on each of the K, Mg, and F atoms are calculated and found to be in good agreement with previous theoretical results. First principles computed phonon dispersions for the cubic KMgF3 are reported for the first time. The imaginary part of frequency-dependent dielectric function is determined by summing over all possible transitions from occupied to unoccupied states and taking the appropriate transition matrix element into account. The Born effective charges computed by linear response within density functional perturbation theory are used together with the mode eigenvectors to decompose the lattice dielectric susceptibility tensor into contributions arising from individual IR-active phonon modes. Our results for the static and optical dielectric constant are in good agreement with previously reported experimental results.

Developing nanostructures with tunable magnetic states is crucial for designing novel data storage and quantum information devices. Using density functional theory, we investigate the thermodynamic stability and magnetic properties of tungsten adsorbed tri-vacancy fluorinated (TVF) graphene. We demonstrate a strong structure-property relationship and its response to external stimuli via defect engineering in graphene-based materials. Complex interplay between defect states and the chemisorbed atom results in a large magnetic moment of 7 μB along with high in-plane magneto-crystalline anisotropy energy (MAE) of 17 meV. Under the influence of electric field, spin crossover effect accompanied by a change in the MAE is observed. The ascribed change in spin-configuration is caused by the modification of exchange coupling between defect states and a change in the occupation of d-orbitals of the metal complex. Our predictions open a promising way towards controlling the magnetic properties in graphene based spintronic and non-volatile memory devices.

Emerging computation- and data-driven approaches are particularly useful for rationally designing materials with targeted properties. Generally, these approaches rely on identifying structure-property relationships by learning from a dataset of sufficiently large number of relevant materials. The learned information can then be used to predict the properties of materials not already in the dataset, thus accelerating the materials design. Herein, we develop a dataset of 1,073 polymers and related materials and make it available at http://khazana.uconn.edu/. This dataset is uniformly prepared using first-principles calculations with structures obtained either from other sources or by using structure search methods. Because the immediate target of this work is to assist the design of high dielectric constant polymers, it is initially designed to include the optimized structures, atomization energies, band gaps, and dielectric constants. It will be progressively expanded by accumulating new materials and including additional properties calculated for the optimized structures provided.

We investigate the electronic structure and elastic properties of TiB 2 and ZrB 2 . The electronic properties are computed within the framework of density functional theory (DFT) based on linear combination of atomic orbitals (LCAO), full potential linearized augmented plane wave (FP-LAPW) and projector augmented wave (PAW) methods while the elastic properties are calculated using the PAW method. The calculated electronic and elastic properties of TiB 2 and ZrB 2 are found to be in good agreement with earlier data. In addition, the theoretical directional and spherically averaged Compton profiles of TiB 2 and ZrB 2 are also computed using the LCAO method. The spherically averaged theoretical values are compared with the first-ever measurement on polycrystalline ZrB 2 . To estimate the charge transfer in ZrB 2 , ionic model based calculations have also been performed which suggest transfer of electron from Zr to B atoms.

Density-functional calculations are used to determine the electronic structure and magnetic properties of dilute magnetic semiconductors with the composition X1−xMnxN (X=Al, Ga, In, x=6.25% and 12.5%). Emphasis is on the interatomic exchange as a function of the Mn–Mn distance. Our superlattice calculations show that the Mn dopants are spin-polarized with a half-metallic band gap and a magnetic moment of 4 μB per Mn atom at x=6.25 and 12.5%. The Mn (3d) bands lie in the band gap but partially hybridize with valence band or N 2p electrons, depending on the group-III element and on the spin direction. To calculate the exchange interaction parameters Jij, we have used a Green-function approach. The interaction between Mn atoms extends over several interatomic interactions and is mediated by nitrogen (2p) electrons. The exchange is always ferromagnetic and largest for the first nearest neighbors, but substantial ferromagnetic interactions persist over Mn–Mn distances up to sixth nearest neighbors in the considered supercell.

The electron momentum density distribution in intermetallic TiCu is measured and compared with calculations based on linear combination of atomic orbitals (LCAO) and spin polarized relativistic Korringa-Kohn-Rostoker (SPR-KKR) methods. The spherically averaged theoretical Compton profiles are in good agreement with the measurement. The better agreement is, however, shown by the SPR-KKR method. To investigate the bonding mechanism in the alloy, the density of states has also been determined from SPR-KKR method.

In this paper, we report the first ever experimental Compton profile study of WS 2 and WSe 2 employing 20 Ci 137 Cs Compton spectrometer. To interpret our experimental data, the electronic properties of these compounds have been determined by linear combination of atomic orbitals, full potential linearised augmented plane-wave and spin polarised relativistic Korringa–Kohn–Rostoker (SPR-KKR) schemes. The band structure calculations show that both the WS 2 and WSe 2 are indirect-gap semiconductors. The SPR-KKR calculations are found to be relatively in poor agreement with the experimental electron momentum densities. The relative nature of bonding in both the dichalcogenides is explained in terms of equal-valence-electron-density profiles, Mulliken's population and valence band charge densities.

We present the first ever Compton scattering study on WxCu1-x(x=0.60, 0.72) alloys. The Compton profile measurements have been made using 20Ci (CS)-C-137 gamma-ray source. The experimental data are compared with the superposition of APW-based Compton profiles of constituent metals. A schematic study on charge transfer has been reported using the experimental valence band Compton profiles of both the alloys, W and Cu. Our first ever data support the charge transfer from W to Cu on alloying, which is also confirmed by our band structure calculations employing exact muffin-tin orbitals method (EMTO). (C) 2007 Elsevier B.V. All rights reserved.

In this paper, we report the energy bands, density of states and the first ever Compton profiles of PbS and PbSe computed in the framework of spin-polarised relativistic Korringa–Kohn–Rostoker and ab initio pseudopotential with Hartree–Fock and density functional theory schemes. In addition, we have computed the energy bands and density of states using full-potential linearised augmented plane wave method. To compare our theoretical data, we have also measured the first ever isotropic Compton profiles at an intermediate resolution using our 740 GBq (20 Ci) 137 Cs Compton spectrometer. The Compton profiles scaled on equal-valence-electron-density show that PbSe is more covalent than PbS, which is in agreement with the present Mulliken's population data.

The effects of strain and hydrogenation on the electronic and magnetic properties of monolayers of Ta based dichalcogenides (TaX2; X = S, Se, Te) are investigated using density-functional theo-ry. We predict a complex scenario of strain-dependent magnetic phase transitions involving par-amagnetic, ferromagnetic, and modulated antiferromagnetic states. Covering one of the two chalcogenide surfaces with hydrogen switches the antiferromagnetic/nonmagnetic TaX2 mono-layers to a semiconductor. Our research opens new pathways towards the manipulation of mag-netic properties for future optoelectronics and spintronics applications., 13 pages, 5 figures

Structural and magnetic properties of the doped terbium manganites (Tb,A)MnO3 (A = Gd, Dy and Ho) have been investigated using first-principles calculations and further confirmed by subse- quent experimental studies. Both computational and experimental studies suggest that compared to the parent material, namely, TbMnO3 (with a magnetic moment of 9.7 /muB for Tb3+) Dy- and Ho- ion substituted TbMnO3 results in an increase in the magnetic moment (< 10.6/muB for Dy3+ and Ho3+). The observed spiral-spin AFM order in TbMnO3 is stable with respect to the dopant substitutions, which modify the Mn-O-Mn bond angles and lead to stronger the ferromagnetic component of the magnetic moment. Given the fact that magnetic ordering in TbMnO3 causes the ferroelectricity, this is an important step in the field of the magnetically driven ferroelectricity in the class of magnetoelectric multiferroics, which traditionally have low magnetic moments due to the predominantly antiferromagnetic order. In addition, the present study reveals important insights on the phenomenological coupling mechanism in detail, which is essential in order to design new materials with enhanced magneto-electric effects at higher temperatures., 6 pages, 5 figures

The question of whether one can systematically identify (previously unknown) ferroelectric phases of a given material is addressed, taking hafnia (HfO$_2$) as an example. Low free energy phases at various pressures and temperatures are identified using a first-principles based structure search algorithm. Ferroelectric phases are then recognized by exploiting group theoretical principles for the symmetry-allowed displacive transitions between non-polar and polar phases. Two orthorhombic polar phases occurring in space groups $Pca2_1$ and $Pmn2_1$ are singled out as the most viable ferroelectric phases of hafnia, as they display low free energies (relative to known non-polar phases), and substantial switchable spontaneous electric polarization. These results provide an explanation for the recently observed surprising ferroelectric behavior of hafnia, and reveal pathways for stabilizing ferroelectric phases of hafnia as well as other compounds., Comment: There are 6 pages of main text (5 figures) and 3 pages of supplemental material (2 figures & 1 table)