The Computational Chemistry Grid (CCG) is a three-year, National Middleware Initiative program to develop cyberinfrastructure for the chemistry community. CCG is led by the University of Kentucky and involves collaborating sites at Louisiana State University, Ohio Supercomputing Center, Texas Advanced Computing Center, and the National Center for Supercomputing Applications. This paper discusses experiences developing the CCG cyberinfrastructure in the first year of the project. Special attention is paid to September 1, 2005. technological issues faced as well as issues raised running the CCG in production. The final section of the paper looks forward to challenges foreseen in the remaining two years.
Dynamics and energy release in benzene/Ar cluster dissociationThe effect of coupled nonreactive modes on laser control of quantum wave packet dynamics J. Chem. Phys. 111, 6864 (1999); 10.1063/1.479978Time-dependent wave packet study of the one atom cage effect in I 2 -Ar Van der Waals complexes A hybrid quantum/classical approach for treating the vibrational and translational motion of the I 2 molecule inside a cold Ar matrix is implemented in the control of vibrational wave packet localization on the excited ͑A͒ electronic surface of I 2 . Quantum control was performed in the weak-field regime at six different temperatures to examine thermal effects on the dynamics of I 2 inside the lattice and on the degree of control that can be achieved for this system. It was found in this study that an increase in temperature from 0 to 75 K leads to a moderate decrease in the degree of control achieved. The role played by I 2 rotation on control was also shown to be minimal under the conditions examined in this work.
Eigenvalues corresponding to the three torsional degrees of freedom were calculated for the water trimer and its deuterated isotopomer in four sets of calculations involving different potential energy surfaces. The four potential surfaces were developed in this work by reparametrization of the CKL function against four sets of ab initio energies calculated with and without counterpoise correction. Transition frequencies corresponding to the low-frequency torsional motions of the trimer were calculated and then compared with those found from experiment to assess the accuracy of each potential energy surface. Although reparametrization of the CKL function to a set of counterpoise-corrected energies yielded transition energies that are in qualitative agreement with those from experiment, reparametrization to another set of counterpoise-corrected energies resulted in highly inaccurate values of the transition energy. As a consequence, our results demonstrate that caution must be exercised in the implementation of the counterpoise method as it does not always lead to more accurate ab initio calculations.
Two Lanczos subspace propagation techniques are discussed in this work and compared with the Chebyshev method applied to the original Hamiltonian matrix. Both procedures involve the use of a reduced propagator in the Lanczos subspace to calculate the solution to the time-dependent Schrodinger equation but differ in thë Ž . way the propagator is evaluated. The LSC Lanczos subspace Chebyshev expresses the propagator in terms of Chebyshev polynomials that are functions of the tridiagonal Ž Hamiltonian matrix in the Lanczos space. In contrast, the LSV Lanczos subspace . variational is implemented by solving the eigenproblem in the Lanczos subspace and then performing a variational expansion of the propagator in the M-dimensional eigenvector space. Although the LSV is the same as the reduced propagator scheme proposed by Park and Light, in the present study the LSV is implemented as a one-step long-time propagator. As a numerical example, the interaction of a molecule with a strong laser pulse is investigated. The Hamiltonian is explicitly time dependent in this case, and thus the stationary formalism is employed in this work to solve the timedependent Schrodinger equation. Application of either the LSC or LSV yields a wavë function in the M-dimensional Lanczos subspace. Nonetheless, the transition amplitudes computed from this wave function are in excellent agreement with those calculated by direct application of the Chebyshev method in the original space used to define the Hamiltonian matrix.
The presence of cirrus clouds introduces complex heating and cooling effects on the atmosphere and can also interfere with remote sensing from satellite-based sensors or from high-altitude aircraft. Detection of cirrus clouds thus provides an opportunity for atmospheric correction to introduce accurate compensation to images of the earth's surface. Previous work on detection and characterization of cirrus clouds have been based on observing spectral signatures on a spectral channel with significant water absorption, or calculation of radiant intensity ratios over a water band to a reference spectral channel.Our proposed approach is based on applying computational homology to characterize the topological properties of cirrus clouds. We utilize an application called JPLEX to study the persistent homology of multi-dimensional simplicial complexes built from available hyperspectral or multispectral data. The technique has been successfully applied to discriminate subtle features in high dimensional noisy data sets. Previous examples include anomaly detection in hyperspectral images. The analysis makes use of the entire multidimensional data set (not just one or a combination of spectral bands) which may offer advantages in discriminating among various cloud types in a scene, as well as determining other characteristics of cirrus clouds such as altitude and thickness. Our initial computational experiment with an AVIRIS scene has demonstrated that JPLEX is able to discriminate between cumulus and cirrus clouds.
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