Converging High-Level Coupled-Cluster Energetics by Monte Carlo Sampling and Moment Expansions

Efficient Monte Carlo sampling by parallel marginalization.

Markov chain Monte Carlo sampling methods often suffer from long correlation times. Consequently, these methods must be run for many steps to generate an independent sample. In this paper, a method is proposed to overcome this difficulty. The method utilizes information from rapidly equilibrating coarse Markov chains that sample marginal distributions of the full system. This is accomplished th...

Christiane Lemieux Monte Carlo and Quasi - Monte Carlo Sampling

Microcanonical Cluster Monte Carlo

I propose a numerical simulation algorithm for statistical systems which combines a microcanonical transfer of energy with global changes in clusters of spins. The advantages of the cluster approach near a critical point augment the speed increases associated with multi-spin coding in the mi-crocanonical approach. The method also provides a limited ability to tune the average cluster size.

Hermite Expansions in Monte-Carlo Computation*

Monte-Carlo computations often yield numerical answers of limited accuracy, and are therefore employed as a last resort. It has been found, however, that some of the limitations of Monte-Carlo methods can be overcome through a judicious use of orthogonal expansions. When a numerical answer is obtained as the expected value of an estimator, expansion of that estimator in a series of orthogonal f...

The energetics of oxide surfaces by quantum Monte Carlo

Density functional theory (DFT) is widely used in surface science, but for some properties the predictions depend strongly on the approximation used for exchange–correlation energy. We note recent suggestions that the widely used generalized gradient approximation (GGA) is inferior to the local density approximation (LDA) for the surface formation energy σ of both transition metals and oxides. ...