First-principle molecular dynamics with ultrasoft pseudopotentials: parallel implementation and application to extended bio-inorganic system

We present a plane-wave ultrasoft pseudopotential implementation of first-principle molecular dynamics, which is well suited to model large molecular systems containing transition metal centers. We describe an efficient strategy for parallelization that includes special features to deal with the augmented charge in the contest of Vanderbilt's ultrasoft pseudopotentials. We also discuss a simple approach to model molecular systems with a net charge and/or large dipole/quadrupole moments. We present test applications to manganese and iron porphyrins representative of a large class of biologically relevant metallorganic systems. Our results show that accurate Density-Functional Theory calculations on systems with several hundred atoms are feasible with access to moderate computational resources.Comment: 29 pages, 4 Postscript figures, revtex

Comment on "Why quantum mechanics cannot be formulated as a Markov process"

In the paper with the above title, D. T. Gillespie [Phys. Rev. A 49, 1607, (1994)] claims that the theory of Markov stochastic processes cannot provide an adequate mathematical framework for quantum mechanics. In conjunction with the specific quantum dynamics considered there, we give a general analysis of the associated dichotomic jump processes. If we assume that Gillespie's "measurement probabilities" \it are \rm the transition probabilities of a stochastic process, then the process must have an invariant (time independent) probability measure. Alternatively, if we demand the probability measure of the process to follow the quantally implemented (via the Born statistical postulate) evolution, then we arrive at the jump process which \it can \rm be interpreted as a Markov process if restricted to a suitable duration time. However, there is no corresponding Markov process consistent with the $Z_2$ event space assumption, if we require its existence for all times $t\in R_+$.Comment: Latex file, resubm. to Phys. Rev.

Neural networks for gamma-hadron separation in MAGIC

Neural networks have proved to be versatile and robust for particle separation in many experiments related to particle astrophysics. We apply these techniques to separate gamma rays from hadrons for the MAGIC Cerenkov Telescope. Two types of neural network architectures have been used for the classi cation task: one is the MultiLayer Perceptron (MLP) based on supervised learning, and the other is the Self-Organising Tree Algorithm (SOTA), which is based on unsupervised learning. We propose a new architecture by combining these two neural networks types to yield better and faster classi cation results for our classi cation problem.Comment: 6 pages, 4 figures, to be published in the Proceedings of the 6th International Symposium ''Frontiers of Fundamental and Computational Physics'' (FFP6), Udine (Italy), Sep. 26-29, 200

A Next-to-Leading-Order Study of Dihadron Production

The production of pairs of hadrons in hadronic collisions is studied using a next-to-leading-order Monte Carlo program based on the phase space slicing technique. Up-to-date fragmentation functions based on fits to LEP data are employed, together with several versions of current parton distribution functions. Good agreement is found with data for the dihadron mass distribution. A comparison is also made with data for the dihadron angular distribution. The scale dependence of the predictions and the dependence on the choices made for the fragmentation and parton distribution functions are also presented. The good agreement between theory and experiment is contrasted to the case for single $\pi^0$ production where significant deviations between theory and experiment have been observed.Comment: 22 pages, 15 figures; 3 references added, one figure modified for clarit