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Shiv Nadar University

Center for Informatics

We investigated the structural, elastic and electronic properties of 4d-transition-metal carbides (ZrC, NbC) and nitrides (ZrN, NbN) and their ternary alloys ZrxNb1-xC and ZrxNb1-xN in the range 0≤x≤1 by using the APW+lo method to solve Kohn-Sham equations. We have used the local density approximation for the exchange and correlation potential. The ground state properties, equilibrium lattice constants, bulk moduli, elastic constants, densities of electron states and band structures are determined and discussed for all compounds and their related ternary alloys. The results are compared to calculations performed for the similar systems and with experiments. A model structure of 16-atoms supercell is used. {\textcopyright} 2009 Elsevier B.V. All rights reserved.

World University root node

Physica B: Condensed Matter

Ab initio studies of structural, elastic and electronic properties of ZrxNb1-xC and ZrxNb1-xN alloys

Spatially resolved images of an individual C60F18 fluorofullerene molecule on Si(100) - 2 × 1 surface have been obtained using scanning tunneling microscopy. Scanning tunneling microscopy results and ab initio calculations show that the fluorofullerene molecules interact with the Si(100) - 2 × 1 surface with F atoms pointing down towards the surface. The adsorption energy of a C60F18 molecule on Si(100) - 2 × 1 surface is $\sim$12. 1 eV, which is much higher than the adsorption energy of the same molecule on Si(111) - 7 × 7 surface (6. 65 eV). C60F18 molecules are located in the troughs in-between the dimer rows occupying the four-dimer site on Si(100) - 2 × 1 surface. {\textcopyright} 2010 Pleiades Publishing, Ltd.

JETP Letters

Initial stage of the adsorption of fluorofullerene molecules on Si surface

Conventional linear & nonlinear tools for classification, regression & data driven modeling are being replaced on a rapid scale by newer techniques & tools based on artificial intelligence and machine learning. While the linear techniques are not applicable for inherently nonlinear problems, newer methods serve as attractive alternatives for solving real life problems. Support Vector Machine (SVM) classifiers are a set of universal feed-forward network based classification algorithms that have been formulated from statistical learning theory and structural risk minimization principle. SVM regression closely follows the classification methodology. In this work recent applications of SVM in Chemo & Bioinformatics will be described with suitable illustrative examples. © 2010 American Institute of Physics.

AIP Conference Proceedings

Applications of support vector machines in chemo and bioinformatics

The optical properties of bare and passivated Znn Sen (n=1-13) clusters have been studied within the framework of time-dependent local density approximation. The atomic structure of the clusters has been obtained using projector augmented wave pseudopotential method, with generalized gradient approximation for the exchange-correlation energy. The small clusters with n up to 5 have two-dimensional (2D) structure and for larger sizes, cagelike 3D structures become favorable. At n=13, the clusters start getting an atom inside the cage to attain bulklike local structure. For the bare clusters, the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) gap increases from a small value for ZnSe dimer and beyond n=3, the variation is small. On the other hand, the HOMO-LUMO gap of the clusters passivated with partially charged hydrogen atom decreases nearly monotonically with increasing size, though the value remains higher compared with that of the bare clusters even for the case of n=13. Further, the optical absorption spectra and the corresponding optical gap have been calculated and a decreasing trend as a function of the increasing cluster size has been obtained. This compares well with the experimental results available on larger clusters in the literature though the calculated values underestimate the optical absorption gap as expected within the local density approximation framework. {\textcopyright} 2009 The American Physical Society.

Physical Review B - Condensed Matter and Materials Physics

Optical properties of zinc selenide clusters from first-principles calculations

Using density functional theory (DFT) calculations, we investigate the specie obtained from the stepwise on-cage P-doping of the silicon fullerene Si20H20. It is found that P-doping decreases the HOMO-LUMO energy gap of the fullerene and could be used to tailor its optical properties. Pairing of P atoms within the fullerene's cage is not favored owing to the repulsion between the phosphorous lone-pairs and the preference for Si-P bond formation. Replacing all the SiH moieties with P affords the dodecahedral P20 fullerene. The fully oxidized P20O20 species is characterized by exceptionally high ionization potential (10.73 eV) and large electron affinity (6.71 eV). {\textcopyright} 2009 Elsevier B.V. All rights reserved.

Journal of Molecular Structure: THEOCHEM

Geometries and electronic structures of phosphorous-doped silicon fullerenes: A DFT study

Abstract: Building accurate quantitative structure-activity relationships (QSAR) is important in drug design, environmental modeling, toxicology, and chemical property prediction. QSAR methods can be utilized to solve mainly two types of problems viz., pattern recognition, (or classification) where output is discrete (i.e. class information), e.g., active or non-active molecule, binding or non-binding molecule etc., and function approximation, (i.e. regression) where the output is continuous (e.g., actual activity prediction). The present review deals with the second type of problem (regression) with specific attention to one of the most effective machine learning procedures, viz. lazy learning. The methodologies of the algorithm along with the relevant technical information are discussed in detail. We also present three real life case studies to briefly outline the typical characteristics of the modeling formalism.

Combinatorial Chemistry and High Throughput Screening

Review on lazy learning regressors and their applications in QSAR

APS March Meeting Abstracts, X36.

Atomic structure of PdNiP bulk metallic glass from ab initio simulations

N-Glycosylation plays a very important role in various processes like quality control of proteins produced in ER, transport of proteins and in disease control.The experimental elucidation of N-Glycosylation sites is expensive and laborious process. In this work we build models for identification of potential N-Glycosylation sites in proteins based on sequence and structural features.The best model has cross validation accuracy rate of 72.81%. © 2009 Springer-Verlag Berlin Heidelberg.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Identification of N-glycosylation sites with sequence and structural features employing random forests

The effects on the electronic band structure of hydrogenated cubic silicon carbide ($\beta$-SiC) nanowires of changes in the diameter and morphology are studied using a semiempirical sp3s* tight-binding approach applied to a supercell model. The results of the calculation of the electronic band structure and electronic density of states obtained are compared with those calculated by density functional theory within local density approximation only for the bulk of $\beta$-SiC. As boundary conditions, we passivated all the Si and C dangling bonds with hydrogen atoms. The results show that although surface morphology modifies the band gap, the change is more systematic with the thickness variation. The energy band gap increases with decreasing diameter in all cases because of quantum confinement, but the scaling is dependent on the morphology (cross-section) of the $\beta$-SiC nanowires. Finally, the calculations show a consistent asymptotical behavior to the crystalline limit when the width of the wires enlarges. {\textcopyright} (2009) Trans Tech Publications, Switzerland.

Journal of Nano Research

Effects of morphology on the electronic properties of hydrogenated silicon carbide nanowires

Defensins represent a class of antimicrobial peptides synthesized in the body acting against various microbes. In this paper we study defensins using a non-linear signal analysis method Recurrence Quantication Analysis (RQA). We used the descriptors calculated employing RQA for the classification of defensins with Random Forest Classifier.The RQA descriptors were able to capture patterns peculiar to defensins leading to an accuracy rate of 78.12% using 10-fold cross validation. © 2009 Springer-Verlag Berlin Heidelberg.