Related papers: An Efficient Monte Carlo Algorithm for Determining…
We study a sequential Monte Carlo algorithm to sample from the Gibbs measure with a non-convex energy function at a low temperature. We use the practical and popular geometric annealing schedule, and use a Langevin diffusion at each…
Surfaces bombarded with low energy ion beams often display development of self assembled patterns and quasi-periodic structures. Kinetic Monte Carlo simulations have been performed to describe ion sputtered Tantalum surfaces. A weak…
An iterative algorithm based on Monte Carlo method is used to model thin film growth of AB type molecule and crystallization. Primarly, PVD technique is investigated since it is one of the most preferred on thin film growth processes. The…
This work investigates how grain boundaries (GBs) modify magnetic exchange interactions in bcc iron, with particular focus on the effect of phosphorus segregation. Using density-functional theory combined with the…
Variational quantum algorithms are poised to have significant impact on high-dimensional optimization, with applications in classical combinatorics, quantum chemistry, and condensed matter. Nevertheless, the optimization landscape of these…
We study structural phase transition of polymer-grafted colloidal particles by Monte Carlo simulations on hard spherical particles. The interaction potential, which has a weak repulsive step outside the hard core, was validated with use of…
Purpose: Very fast Monte Carlo (MC) simulations of proton transport have been implemented recently on GPUs. However, these usually use simplified models for non-elastic (NE) proton-nucleus interactions. Our primary goal is to build a…
We present an efficient method for anealing 3d metallic grains using Gradient Weighted Moving Finite Elements (GWMFE). The initial grain microstructure is generated from Monte-Carlo evolution of a an effective discrete model on the initial…
We outline how auxiliary-field quantum Monte Carlo (AFQMC) can leverage graphical processing units (GPUs) to accelerate the simulation of solid state sytems. By exploiting conservation of crystal momentum in the one- and two-electron…
Quantum Monte Carlo methods find fruitful application in large shell model problems. These methods reduce the imaginary-time many-body evolution operator to a coherent superposition of one-body evolutions in a fluctuating one-body field;…
We perform quantum Monte Carlo (QMC) calculations to determine minimum energy pathways of simple chemical reactions, and compare the computed geometries and reaction barriers with those obtained with density functional theory (DFT) and…
Variational Monte Carlo and Green's function Monte Carlo are powerful tools for calculations of properties of light nuclei using realistic two-nucleon and three-nucleon potentials. Recently the GFMC method has been extended to multiple…
We present a force-biased Monte Carlo (FMC) method for structural modeling of transition metal clusters of Fe, Ni, and Cu with 5 to 60 atoms. By employing the Finnis-Sinclair potential for Fe and the Sutton-Chen potential for Ni and Cu, the…
A polycrystalline graphene consists of perfect domains tilted at angle {\alpha} to each other and separated by the grain boundaries (GB). These nearly one-dimensional regions consist in turn of elementary topological defects, 5-pentagons…
The Markov chain Monte Carlo method is a versatile tool in statistical physics to evaluate multi-dimensional integrals numerically. For the method to work effectively, we must consider the following key issues: the choice of ensemble, the…
The grain boundary (GB) energy is a quantity of fundamental importance for understanding several key properties of graphene. Here we present a comprehensive theoretical and numerical study of the entire space of symmetric and asymmetric…
The recently developed auxiliary field diffusion Monte Carlo method is applied to compute the equation of state and the compressibility of neutron matter. By combining diffusion Monte Carlo for the spatial degrees of freedom and auxiliary…
Describing correlated electron systems near phase transitions has been a major challenge in computational condensed-matter physics. In this paper, we apply highly accurate fixed node quantum Monte Carlo techniques, which directly work with…
Phase transformations in metallic grain boundaries (GBs) present significant fundamental interest in the context of thermodynamics of low-dimensional physical systems. We report on atomistic computer simulations of the Cu-Ag system that…
Grain boundary (GB) segregation is a powerful approach for optimizing the thermal and mechanical properties of metal alloys. In this study, we report significant GB interstitial segregation in a representative substitutional binary alloy…