<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

Center for Informatics

A machine learning algorithm using evolutionary algorithms and Support Vector Machines is presented. The kernel function of support vector machines are evolved using recently introduced Gene Expression Programming algorithms. This technique trains a support vector machine with the kernel function most suitable for the training data set rather than pre-specifying the kernel function. The fitness of the kernel is measured by calculating cross validation accuracy. SVM trained with the fittest kernels is then used to classify previously unseen data. The algorithm is elucidated using preliminary case studies for classification of cancer data and bank transaction data set. It is shown that the Evolutionary Support Vector Machine has good generalization properties when compared with Support Vector Machines using standard (polynomial and radial basis) kernel functions. © 2006 IEEE.

World University root node

Proceedings - 2006 14th International Conference on Advanced Computing and Communications, ADCOM 2006

Evolutionary selection of Kernels in support vector machines

Electron correlation in chromium-doped silicon cluster (Cr@Si12) in its neutral, positively and negatively charged states with different nuclear configurations is investigated by means of quantum Monte Carlo methods. It is found that the correlation energy per electron is independent of whether the state is charged or not and about -1 eV for each of these three states. The total binding energy of the neutral state per atom is 3.5 eV, which is divided into the Hartree-Fock contribution nearly equal to 1.2 eV and the correlation contribution as large as 2.3 eV. In the Hartree-Fock approximation, the ionization energy is 6.9 eV and the electron affinity is 2.7 eV. Correlation increases the ionization energy by 1.7eV and the electron affinity by 1.4eV. {\textcopyright} 2006 The Japan Institute of Metals.

Materials Transactions

Quantum Monte Carlo study of electron correlation in chromium-doped silicon cluster Cr@Si12

VIJAY KUMAR FOUNDATION CHENNAI (INDIA)

Interaction of Gases on Single-Wall Carbon Nanotubes

A novel method employing a unique combination of wavelet based local singularity analysis and support vector machines (SVM) classification is described and illustrated by considering the case example of flow regime identification in gas-liquid stirred tank equipped with Rushton turbine. Pressure fluctuations time series data obtained at different operating conditions were first analyzed to obtain the distribution of local Hölder exponents' estimates. The relevant features from this distribution were then used as input data to the SVM classifier. Employing this method we could classify flow regimes with 98% accuracy. The results highlight the fact that the local scaling behavior of a given regime follows a distinct pattern. Further, the singularity features can be employed by intelligent machine learning based algorithms like SVM for successful online regime identification. The method can be readily applied to the other multiphase systems like bubble column, fluidized bed, etc. © 2005 Elsevier Ltd. All rights reserved.

Chemical Engineering Science

A novel local singularity distribution based method for flow regime identification: Gas-liquid stirred vessel with Rushton turbine

Current Opinion in Solid State and Materials Science

Computational materials science

Using first principles calculations with ultrasoft pseudopotentials, we study the thick dependence of the atomic and electronic structures of (110) slabs of Ti O2 rutile having one to five layers. Thin slabs with an even and odd number of layers show significantly different structural characteristics and electronic properties that can affect photocatalysis as well as the catalytic behavior of supported clusters. We discuss the origin of the oscillating band gap for both an even and odd number of layers and the effects on the atomic, electronic and magnetic properties of octahedral M6 and icosahedral M13 (M=Pd,Rh) clusters deposited on stoichiometric slabs with two and three layers. Calculations have also been carried out for a Pd atom deposited on different sites of a two layer slab. These results show that the bridging oxygen atoms are most reactive and preferred for adsorption. The adsorption energy of a Pd atom on the bridging site has only a weak dependence on the thickness of the slab. However, the adsorption energy of a cluster supported on a three-layer slab is significantly higher than the value for a two-layer slab due to significant structural differences and this alters the magnetic and electronic properties of the supported clusters. The magnetic moments of Pd clusters are reduced after interaction with the support. However, for Rh clusters there is an increase in the magnetic moment. In general we find that the cluster-support interactions affect mainly the cluster and support atoms that are in contact at the interface. The variation in the band gap with slab thickness can, however, lead to metallic character of the slab after cluster adsorption and this could have important consequences for catalysis. {\textcopyright} 2006 The American Physical Society.

Physical Review B - Condensed Matter and Materials Physics

Thickness dependence of the atomic and electronic structures of Ti O2 rutile (110) slabs and the effects on the electronic and magnetic properties of supported clusters of Pd and Rh

The growth behavior of metal-doped silicon clusters MSi n, M=Ti, Zr and Hf and n=8-16 is studied using an ab initio ultrasoft pseudopotential plane wave method and the generalized gradient approximation for the exchange-correlation energy. For n=8-12, we find basketlike open structures to be most favorable, while for n=13-16, the metal atom is completely surrounded by silicon atoms. These results are in excellent agreement with the observed reactivity of these clusters. Our results suggest continuous aggregation until n=16, which is the optimal cage for the metal-encapsulated silicon clusters with these elements. Further calculations have been done on cation and anion clusters using the Gaussian method. The calculated electron affinities agree well with experimental results in the range of n=12-16 while the calculated values for smaller clusters are higher. Raman activity and infrared spectra have been calculated for selected clusters. These could help in the identification of the structures of these clusters from experiments. {\textcopyright}2005 The American Physical Society.

Growth behavior of metal-doped silicon clusters Si nM (M=Ti,Zr,Hf; n=8-16)

APS March Meeting Abstracts, N26.

Hydrogenated silicon fullerenes and endohedral dopings

First-principles calculations on stoichiometric MonS2n and WnS2n(n=1-8) clusters show that their lowest energy atomic structures are dominated by a central core of metal atoms while the S atoms cap this core. We discuss in detail terminal, edge and face capping of tetrahedral and octahedral Mo clusters by sulfur atoms for off-stoichiometric compositions also. Capping on the terminal sites is found to be least favorable. Edge capped Mo4S6 and face capped Mo6S8 clusters have high symmetries and high stability among all the clusters we have studied. We find that magnetic isomers are lowest in energy for some clusters though bulk MS2 (M=Mo and W) is nonmagnetic. Our results on Mo6S8+p(p=0-6) clusters further show that Mo6S12 is magic with 4$\mu$B magnetic moment. The origin of magnetism and bonding nature in these clusters is discussed. Our finding could lead to the possibility of magnetic clusters of a variety of nonmagnetic transition-metal compounds and their interesting assemblies. {\textcopyright} 2005 The American Physical Society.

Physical Review A - Atomic, Molecular and Optical Physics

Atomic structures and magnetism in small MoS2 and WS2 clusters

Nanoparticles of (CdSe)n are found extremely stable at n = 33 and 34 with structures distinctively different from the bulk fragments. They grow exclusively in large amount with a simple solution method. The diameter is determined as 1.5 nm. Such ultra-stable nanoparticles had been predicted both theoretically and experimentally after the discovery of carbon fullerenes, but not been produced macroscopically in any other element or compound system. First-principles calculations predict that the structures of (CdSe)33 and (CdSe)34 are puckered (CdSe)28-cages accommodating respectively (CdSe)5 an (CdSe)6 inside to form a three-dimensional network of essentially hetero-polar sp3-bonding. {\textcopyright} EDP Sciences, Societ{\`{a}} Italiana di Fisica, Springer-Verlag 2005.