Confinement Effects of Solvation on a Molecule Physisorbed on a Metal Particle

We describe and present results of the implementation of the surface and volume polarization for electrostatics~(SVPE) and the iso-density surface solvation models. Unlike most other implementation of the solvation models where the solute and the solvent are described with multiple numerical representation, our implementation uses a multiresolution, adaptive multiwavelet basis to describe both solute and the solvent. This requires reformulation to use integral equations throughout as well as a conscious management of numerical properties of the basis. Likewise, we investigate the effects of solvation on the static properties of a molecule physisorbed on a spherical particle, modeled as a polarizable continuum colloid with a static dielectric constant. The effective polarizability of the physisorbed molecule is enhanced by a factor of 105 in vacuo and by only 102 when solvated. The variation of the polarizability of the molecules with respect to the changes in their environment illustrates the importance of electrostatic interaction in the enhancement of the effective polarizability. Finally, we investigated the optical properties of 1.4-phenylenedinitrene and 4,4\u27-stilbenedinitrene biraradical molecules. Using our computational model, we establish the structure property relationship in biradical organic compounds. The spin splitting is shown to be inversely proportional to the separation between the two spin carrying centers and is partly driven by the Coulombic interaction. The intense peaks on the absorption spectra is the result of the mixing of transitions from the spin carrying centers with those of pi origin

A Computational Chemistry Study of Spin Traps.

Many defects in physiological processes are due to free radical damage: reactive oxygen species, nitric oxide, and hydroxyl radicals have been implicated in the parthenogenesis of cancer, diabetes mellitus, and rheumatoid arthritis. We herein characterize the phenyl-N-ter-butyl nitrone (PBN) type spin traps in conjunction with the most studied dimethyl-1-pyrroline-N-oxide (DMPO) type spin traps using the hydroxyl radical. In this study, theoretical calculations are carried out on the two main types of spin traps (DMPO and PBN) at the density functional theory level (DFT). The energies of the optimized structures, hyperfine calculations in gaseous and aqueous phases of the spin traps and the hydroxyl radical adduct are calculated at the B3LYP correlation and at the 6-31G (d) and 6-311G (2df, p) basis sets respectively. The dielectric effect on the performance of the spin trap is determined using the polarized continuum model. Calculations show a localization of spin densities in both cases. However, DMPO spin traps are shown to be more stable and more interactive in aqueous environment

MADNESS: A Multiresolution, Adaptive Numerical Environment for Scientific Simulation

MADNESS (multiresolution adaptive numerical environment for scientific simulation) is a high-level software environment for solving integral and differential equations in many dimensions that uses adaptive and fast harmonic analysis methods with guaranteed precision based on multiresolution analysis and separated representations. Underpinning the numerical capabilities is a powerful petascale parallel programming environment that aims to increase both programmer productivity and code scalability. This paper describes the features and capabilities of MADNESS and briefly discusses some current applications in chemistry and several areas of physics

Accuracy of two-particle N -representability conditions for describing different spin states and the singlet-triplet gap in the linear acene series

Variational two-electron reduced-density-matrix (2-RDM) methods can provide a reference-independent description of the electronic structure of strongly correlated molecules and materials. These methods represent one of few ways of performing large active-space-based computations that are beyond the scope of conventional configuration-interaction-based approaches. However, implementations of the method are quite rare, making it difficult for the quantum chemistry community to assess the utility of the approach. Here, we discuss an open-shell implementation of the variational 2-RDM method and explore its ability to describe different spin states in several model systems, including linear hydrogen chains and linear acenes. The largest calculations considered are comparable to complete-active-space computations with 50 electrons in 50 orbitals

Large-Scale Variational Two-Electron Reduced-Density-Matrix-Driven Complete Active Space Self-Consistent Field Methods

A large-scale implementation of the complete active space self-consistent field (CASSCF) method is presented. The active space is described using the variational two-electron reduced-density-matrix (v2RDM) approach, and the algorithm is applicable to much larger active spaces than can be treated using configuration-interaction-driven methods. Density fitting or Cholesky decomposition approximations to the electron repulsion integral tensor allow for the simultaneous optimization of large numbers of external orbitals. We have tested the implementation by evaluating singlet–triplet energy gaps in the linear polyacene series and two dinitrene biradical compounds. For the acene series, we report computations that involve active spaces consisting of as many as 50 electrons in 50 orbitals and the simultaneous optimization of 1892 orbitals. For the dinitrene compounds, we find that the singlet–triplet gaps obtained from v2RDM-driven CASSCF with partial three-electron <i>N</i>-representability conditions agree with those obtained from configuration-interaction-driven approaches to within one-third of 1 kcal mol^–1. When enforcing only the two-electron <i>N</i>-representability conditions, v2RDM-driven CASSCF yields less accurate singlet–triplet energy gaps in these systems, but the quality of the results is still far superior to those obtained from standard single-reference approaches

MADNESS: a multiresolution, adaptive numerical environment for scientific simulation

MADNESS (multiresolution adaptive numerical environment for scientific simulation) is a high-level software environment for solving integral and differential equations in many dimensions that uses adaptive and fast harmonic analysis methods with guaranteed precision that are based on multiresolution analysis and separated representations. Underpinning the numerical capabilities is a powerful petascale parallel programming environment that aims to increase both programmer productivity and code scalability. This paper describes the features and capabilities of MADNESS and briefly discusses some current applications in chemistry and several areas of physics