We have developed a technique to control the placement of atoms in an optical lattice by using a superlattice comprising two separately manipulated, periodic optical potentials with commensurate periods. We demonstrate selective loading of Bose-condensed 87 Rb atoms into every third site of a one-dimensional optical lattice. Our technique provides atoms with wide separation yet tight confinement, useful properties for neutral-atom implementations of quantum computing. Interference of atoms released from the optical lattice and optical Bragg reflection from the atoms reveal the tight confinement and wide separation provided by the patterned filling.

We introduce a quantum mechanical polarizable force field (QMPFF) fitted solely to QM data at the MP2͞aTZ(-hp) level. Atomic charge density is modeled by point-charge nuclei and floating exponentially shaped electron clouds. The functional form of interaction energy parallels quantum mechanics by including electrostatic, exchange, induction, and dispersion terms. Separate fitting of each term to the counterpart calculated from high-quality QM data ensures high transferability of QMPFF parameters to different molecular environments, as well as accurate fit to a broad range of experimental data in both gas and liquid phases. QMPFF, which is much more efficient than ab initio QM, is optimized for the accurate simulation of biomolecular systems and the design of drugs.drug design ͉ quantum mechanics A ccurate simulation of intermolecular interactions is essential in computational studies of chemical and biological systems ranging from multimer spectroscopy in molecular beams, atomsurface interactions, and catalyzed chemical reactions to protein folding and rational drug design. The most reliable and consistent means for such simulations would be to directly use quantum mechanics. However, this is much too computationally demanding, mandating instead the use of a force field, in which the molecular potential surface is approximated by simple analytical formulas. Commonly used force fields including CHARMM, OPLS-AA, MMFF, and AMBER (1-4) originated with Lifson's and Warshel's (5) consistent force field; they all use two basic types of interactions, bonded and nonbonded. The bonded terms are usually modeled formally as functions of stretching, bending, and torsion, whereas the nonbonded components are more physically grounded and involve electrostatic and van der Waals potentials. Electrostatics is described in terms of fixed point charges, and the van der Waals interaction is usually approximated by the classical Leonard-Jones ''12-6'' potential or its modifications. Empirical parameters that shape the various functional forms are found by fitting to low-level quantum mechanical (QM) and͞or experimental data for simple molecules and their interactions in the solid and liquid phases.Although such force fields have been quite successful in modeling a wide variety of molecular systems, there are significant problems in simulation of liquid-phase solutes (6). These force fields have many possible defects including oversimplified treatment of bonded interactions and approximation of charge distributions by point charges with consequent neglect of charge penetration effects, nonadiabatic motions, and other QM features of intra-and intermolecular interactions. However, the most serious defect is recognized to be the failure to incorporate electronic polarization at a fundamental level, which is especially important in a polar medium such as water. To allow for the effects of polarization, the standard nonpolarizable force fields fit the mean field of the liquid by artificially increased dipole moments, deformed molecular geo...

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We have recently introduced a quantum mechanical polarizable force field (QMPFF) fitted solely to high-level quantum mechanical data for simulations of biomolecular systems. Here, we present an improved form of the force field, QMPFF2, and apply it to simulations of liquid water. The results of the simulations show excellent agreement with a variety of experimental thermodynamic and structural data, as good or better than that provided by specialized water potentials. In particular, QMPFF2 is the only ab initio force field to accurately reproduce the anomalous temperature dependence of water density to our knowledge. The ability of the same force field to successfully simulate the properties of both organic molecules and water suggests it will be useful for simulations of proteins and protein-ligand interactions in the aqueous environment.G eneral-purpose force fields, from Levitt's early protein potential (1) to modern models such as CHARMM, OPLS-AA, MMFF, and AMBER (2-5), which approximate molecular potentials by simple analytical formulas, are in wide use for computational studies of biological systems ranging from the simplest molecular clusters to large complexes involving proteins. In the latter case, the investigations encounter serious computational problems, primarily related to proper conformational sampling and adequate treatment of the long-range intermolecular interactions; however, with advancements in simulation methodologies and the increase in computer speed these difficulties are alleviated so the accuracy of the underlying models becomes the dominant factor.Protein and protein-ligand interactions usually take place in an aqueous environment, which contributes critically to their energetics, e.g., by hydrogen bonding and the hydrophobic effect. Hence, a force field should accurately reproduce the properties of both organic compounds and water if it is to be used for precise calculations of protein-ligand binding, as required for example in drug-design applications. Moreover, the quality of the applications of a force field to water can be considered as a criterion for the accuracy of the approach as a whole. Hence, it is disconcerting that no general-purpose force field has previously succeeded in accurately describing key properties of liquid water.On the other hand, impressive progress has been made in theoretical studies using specialized water potentials. Many of these potentials are empirical, i.e., they have been fitted to experimental data on the thermodynamics and kinetics of liquid water and in some cases ice. The most advanced of these models, such as the pairwise additive TIP5P (6) and polarizable (7-9) potentials, generally provide an accurate description of the most important properties of water and͞or ice. However, no one model is yet able to reproduce in detail the diversity of thermodynamic and kinetic experimental data on both gas and condensed phases under a range of conditions. Moreover, these empirical water potentials cannot be transferred to more general molecular systems ...

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We have developed PLASS (Protein-Ligand Affinity Statistical Score), a pair-wise potential of mean-force for rapid estimation of the binding affinity of a ligand molecule to a protein active site. This scoring function is derived from the frequency of occurrence of atom-type pairs in crystallographic complexes taken from the Protein Data Bank (PDB). Statistical distributions are converted into distance-dependent contributions to the Gibbs free interaction energy for 10 atomic types using the Boltzmann hypothesis, with only one adjustable parameter. For a representative set of 72 protein-ligand structures, PLASS scores correlate well with the experimentally measured dissociation constants: a correlation coefficient R of 0.82 and RMS error of 2.0 kcal/mol. Such high accuracy results from our novel treatment of the volume correction term, which takes into account the inhomogeneous properties of the protein-ligand complexes. PLASS is able to rank reliably the affinity of complexes which have as much diversity as in the PDB.

A continuous time infinite horizon linear quadratic regulator with input constraints is studied. On the theoretical side, optimality conditions, both in the open loop and feedback form, are shown together with smoothness of the value function and local Lipschitz continuity of the optimal feedback. Arguments are self-contained, use basic ideas of convex conjugacy, and in particular, use a dual optimal control problem. A method of calculating the optimal and stabilizing feedback without relying on discrete optimization is outlined.

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