The Sign Learning Kink (SiLK) based Quantum Monte Carlo (QMC) method is used to calculate the ab initio ground state energies for multiple geometries of the H2O, N2, and F2 molecules. The method is based on Feynman's path integral formulation of quantum mechanics and has two stages. The first stage is called the learning stage and reduces the well-known QMC minus sign problem by optimizing the ...

We carry out a theoretical investigation of overpressurized superfluid phases He-4 by means quantum Monte Carlo (QMC) simulations. As function density, we study structural and properties, estimate the energy roton excitation inverting imaginary-time density correlation functions computed QMC, using Maximum Entropy. pressure at which vanishes to be about 100 bars, identify with spinodal i.e., up...

Quasi-Monte Carlo (QMC) methods have begun to displace ordinary Monte Carlo (MC) methods in many practical problems. It is natural and obvious to combine QMC methods with traditional variance reduction techniques used in MC sampling, such as control variates. There can, however, be some surprises. The optimal control variate coefficient for QMC methods is not in general the same as for MC. Usin...

We present density-functional theory (DFT) and quantum Monte Carlo (QMC) calculations designed to resolve experimental and theoretical controversies over the optical properties of H-terminated C nanoparticles (diamondoids). The QMC results follow the trends of well-converged plane-wave DFT calculations for the size dependence of the optical gap, but they predict gaps that are 1-2 eV higher. The...

In this paper we study the possibility to use the Sobol’ and Halton quasi-random number sequences (QRNs) in solving the BarkerFerry (B-F) equation which accounts for the quantum character of the electron-phonon interaction in semiconductors. The quasi-Monte Carlo (QMC) solutions obtained by QRNs are compared with the Monte Carlo (MC) solutions in case when the scalable parallel random number ge...

the length of equal minimal and maximal blocks has eected on logarithm-scale logarithm against sequential function on variance and bias of de-trended uctuation analysis, by using quasi monte carlo(qmc) simulation and cholesky decompositions, minimal block couple and maximal are founded which are minimum the summation of mean error square in horest power.

Optimization in the presence of noise is a difficult task. Most of the optimization methods available are totally deterministic in nature, and, when applied to problems affected by noise, they are either unable to reach an optimum or they may reach a false one. In this Letter we present a novel optimization scheme, called the stochastic gradient approximation (SGA). The method has its roots in ...

We show the feasibility of using quantum Monte Carlo (QMC) to compute benchmark energies for configuration samples of thermal-equilibrium water clusters and the bulk liquid containing up to 64 molecules. Evidence that the accuracy of these benchmarks approaches that of basis-set converged coupled-cluster calculations is noted. We illustrate the usefulness of the benchmarks by using them to anal...

Ground state properties of condensed helium are calculated using the path integral ground state (PIGS) method. A fourth-order approximation is used as short (imaginary) time propagator. We compare our results with those obtained with other quantum Monte Carlo (QMC) techniques and different propagators. For this particular application, we find that the fourth-order propagator performs comparably...