Nuclear Resonance Vibrational Spectroscopy of Iron Sulfur Proteins

Nuclear inelastic scattering in conjunction with density functional theory (DFT) calculations has been applied for the identification of vibrational modes of the high-spin ferric and the high-spin ferrous iron-sulfur center of a rubredoxin-type protein from the thermophylic bacterium Pyrococcus abysii

DFT Calculations as a Tool to Analyse Quadrupole Splittings of Spin Crossover Fe(II) complexes

Density functional methods have been applied to calculate the quadrupole splitting of a series of iron(II) spin crossover complexes. Experimental and calculated values are in reasonable agreement. In one case spin-orbit coupling is necessary to explain the very small quadrupole splitting value of 0.77 mm/s at 293 K for a high-spin isomer

Mechanism of hydrogenolysis of an iridium-methyl bond: Evidence for a methane complex intermediate

Evidence for key σ-complex intermediates in the hydrogenolysis of the iridium-methyl bond of (PONOP)Ir(H)(Me)+ (1) [PONOP = 2,6-bis(di-tert-butylphosphinito)pyridine] has been obtained. The initially formed η2-H2 complex, 2, was directly observed upon treatment of 1 with H2, and evidence for reversible formation of a σ-methane complex, 5, was obtained through deuterium scrambling from η2-D2 in 2-d2 into the methyl group of 2 prior to methane loss. This sequence of reactions was modeled by density functional theory calculations. The transition state for formation of 5 from 2 showed significant shortening of the Ir-H bond for the hydrogen being transferred; no true Ir(V) trihydride intermediate could be located. Barriers to methane loss from 2 were compared to those of 1 and the six-coordinate species (PONOP)Ir(H)(Me)(CO)+ and (PONOP)Ir(H)(Me)(Cl). © 2013 American Chemical Society.Peer Reviewe

Study of the Ground-State Geometry of Silicon Clusters Using Artificial Neural Networks

Theoretical determination of the ground-state geometry of Si clusters is a difficult task. As the number of local minima grows exponentially with the number of atoms, to find the global minimum is a real challenge. One may start the search procedure from a random distribution of atoms but it is probably wiser to make use of any available information to restrict the search space. Here, we introduce a new approach, the Assisted Genetic Optimization (AGO) that couples an Artificial Neural Network (ANN) to a Genetic Algorithm (GA). Using available information on small Silicon clusters, we trained an ANN to predict good starting points (initial population) for the GA. AGO is applied to Si10 and Si20 and compared to pure GA. Our results indicate: i) AGO is, at least, 5 times faster than pure GA in our test case; ii) ANN training can be made very fast and successfully plays the role of an experienced investigator; iii) AGO can easily be adapted to other optimization problems