A generalized operational formula based on total electronic densities to obtain 3D pictures of the dual descriptor to reveal nucleophilic and electrophilic sites accurately on closed-shell molecules

Indexación: Wiley Online Library. Online Version of Record published before inclusion in an issue http://onlinelibrary.wiley.com/doi/10.1002/jcc.24453/fullBy means of the conceptual density functional theory, the so-called dual descriptor (DD) has been adapted to be used in any closed-shell molecule that presents degeneracy in its frontier molecular orbitals. The latter is of paramount importance because a correct description of local reactivity will allow to predict the most favorable sites on a molecule to undergo nucleophilic or electrophilic attacks; on the contrary, an incomplete description of local reactivity might have serio us consequences, particularly for those experimental chemists that have the need of getting an insight about reactivity of chemical reagents before using them in synthesis to obtain a new compound. In the present work, the old approach based only on electronic densities of frontier molecular orbitals is replaced by the most accurate procedure that implies the use of total electronic densities thus keeping consistency with the essential principle of the DFT in which the electronic density is the fundamental variable and not the molecular orbitals. As a result of the present work, the DD will be able to properly describe local reactivities only in terms of total electronic densities. To test the proposed operational formula, 12 very common molecules were selected as the original definition of the DD was not able to describe their local reactivities properly. The ethylene molecule was additionally used to test the capability of the proposed operational formula to reveal a correct local reactivity even in absence of degeneracy in frontier molecular orbitals.http://onlinelibrary.wiley.com/doi/10.1002/jcc.24453/ful

The Tensor Networks Anthology: Simulation techniques for many-body quantum lattice systems

We present a compendium of numerical simulation techniques, based on tensor network methods, aiming to address problems of many-body quantum mechanics on a classical computer. The core setting of this anthology are lattice problems in low spatial dimension at finite size, a physical scenario where tensor network methods, both Density Matrix Renormalization Group and beyond, have long proven to be winning strategies. Here we explore in detail the numerical frameworks and methods employed to deal with low-dimension physical setups, from a computational physics perspective. We focus on symmetries and closed-system simulations in arbitrary boundary conditions, while discussing the numerical data structures and linear algebra manipulation routines involved, which form the core libraries of any tensor network code. At a higher level, we put the spotlight on loop-free network geometries, discussing their advantages, and presenting in detail algorithms to simulate low-energy equilibrium states. Accompanied by discussions of data structures, numerical techniques and performance, this anthology serves as a programmer's companion, as well as a self-contained introduction and review of the basic and selected advanced concepts in tensor networks, including examples of their applications.Comment: 115 pages, 56 figure

Holonomic Quantum Computation

We show that the notion of generalized Berry phase i.e., non-abelian holonomy, can be used for enabling quantum computation. The computational space is realized by a $n$-fold degenerate eigenspace of a family of Hamiltonians parametrized by a manifold $\cal M$. The point of $\cal M$ represents classical configuration of control fields and, for multi-partite systems, couplings between subsystem. Adiabatic loops in the control $\cal M$ induce non trivial unitary transformations on the computational space. For a generic system it is shown that this mechanism allows for universal quantum computation by composing a generic pair of loops in $\cal M.$Comment: Presentation improved, accepted by Phys. Lett. A, 5 pages LaTeX, no figure

Protein design: A perspective from simple tractable models

We review the recent progress in computational approaches to protein design which builds on advances in statistical-mechanical protein folding theory. In particular, we evaluate the degeneracy of the protein code (i.e. how many sequences fold into a given conformation) and outline a simple condition for ''designability`` in a protein model. From this point of view we discuss several popular protein models that were used for sequence design by several authors. We evaluate the strengths and weaknesses of popular approaches based on stochastic optimization in sequence space and discuss possible ways to improve them to bring them closer to experiment. We also discuss how sequence design affects folding and point out to some features of proteins that can be deigned ''in'' or designed ''out''}Comment: 12 pages three figure

On particle filters applied to electricity load forecasting

We are interested in the online prediction of the electricity load, within the Bayesian framework of dynamic models. We offer a review of sequential Monte Carlo methods, and provide the calculations needed for the derivation of so-called particles filters. We also discuss the practical issues arising from their use, and some of the variants proposed in the literature to deal with them, giving detailed algorithms whenever possible for an easy implementation. We propose an additional step to help make basic particle filters more robust with regard to outlying observations. Finally we use such a particle filter to estimate a state-space model that includes exogenous variables in order to forecast the electricity load for the customers of the French electricity company \'Electricit\'e de France and discuss the various results obtained

Learning Loosely Connected Markov Random Fields

We consider the structure learning problem for graphical models that we call loosely connected Markov random fields, in which the number of short paths between any pair of nodes is small, and present a new conditional independence test based algorithm for learning the underlying graph structure. The novel maximization step in our algorithm ensures that the true edges are detected correctly even when there are short cycles in the graph. The number of samples required by our algorithm is C*log p, where p is the size of the graph and the constant C depends on the parameters of the model. We show that several previously studied models are examples of loosely connected Markov random fields, and our algorithm achieves the same or lower computational complexity than the previously designed algorithms for individual cases. We also get new results for more general graphical models, in particular, our algorithm learns general Ising models on the Erdos-Renyi random graph G(p, c/p) correctly with running time O(np^5).Comment: 45 pages, minor revisio