Global Optimization: Quantum Thermal Annealing with Path Integral Monte Carlo

Global Optimization: Quantum Thermal Annealing with Path Integral Monte Carlo

We investigate a new method (QTA-PIMC) for global optimization on complex potential energy surfaces which combines the path integral Monte Carlo method with quantum and thermal annealing. This method is applied to the BLN protein model (Honeycutt, J. D.; Thirumalai, D. Biopolymers 1992, 32, 695). We show that this new approach outperforms simulated (thermal) annealing (SA) and that in fact SA i...

Annealing evolutionary stochastic approximation Monte Carlo for global optimization

Annealing Stochastic Approximation Monte Carlo for Global Optimization

During the past several decades, simulated annealing (Kirkpatrick et al., 1983) and the genetic algorithm (Holland, 1975; Goldberg, 1989) have been applied successfully by many authors to highly complex optimization problems in different fields of sciences and engineering. In spite of their successes, both algorithms suffer from some difficulties in convergence to the global optima. Suppose tha...

Path integral Monte Carlo study of quantum-hard sphere solids.

A path integral study of the fcc, hcp, and bcc quantum hard-sphere solids is presented. Ranges of densities within the interval of reduced de Broglie wavelengths 0.2≤λB(*)≤0.8 have been analyzed using Monte Carlo simulations with Cao-Berne propagator. Energies, pressures, and structural quantities (pair radial correlation functions, centroid structure factors, and Steinhardt order parameters) h...

Permutation sampling in Path Integral Monte Carlo

Abstract A simple algorithm is described to sample permutations of identical particles in Path Integral Monte Carlo (PIMC) simulations of continuum many-body systems. The sampling strategy illustrated here is fairly general, and can be easily incorporated in any PIMC implementation based on the staging algorithm. Although it is similar in spirit to an existing prescription, it differs from it i...