Monte Carlo molecular modeling

About: Monte Carlo molecular modeling is a research topic. Over the lifetime, 11307 publications have been published within this topic receiving 409122 citations.

Validation of GEANT4, an object-oriented Monte Carlo toolkit, for simulations in medical physics.

Abstract: GEANT4 (GEometry ANd Tracking 4) is an object-oriented Monte Carlo simulation toolkit that has been developed by a worldwide collaboration of scientists. It simulates the passage of particles through matter. In order to validate GEANT4 for medical physics applications, different simulations are conducted. The results are compared to published results based on three Monte Carlo codes widely used in medical physics: MCNP, EGS4, and EGSnrc. When possible, the simulation results are also compared to experimental data. Different geometries are tested (multilayer and homogeneous phantoms), different sources considered (point-source and broad parallel beam), and different primary particles simulated (photons and electrons) at different energies. For the heterogeneous media, there are notable differences between the Monte Carlo codes reaching up to over 5% in relative difference. For the monoenergetic electrons in a homogeneous medium, the difference between GEANT4 and the experimental measurements is similar to the difference between EGSnrc and the experimental measurements; for the depth-dose curves, the difference expressed as a fraction of the peak dose is always smaller than 4%. We conclude that GEANT4 is a promising Monte Carlo simulation toolkit for low-energy medical applications.

Backward Simulation Methods for Monte Carlo Statistical Inference

Abstract: Monte Carlo methods, in particular those based on Markov chains and on interacting particle systems, are by now tools that are routinely used in machine learning. These methods have had a profound impact on statistical inference in a wide range of application areas where probabilistic models are used. Moreover, there are many algorithms in machine learning which are based on the idea of processing the data sequentially, first in the forward direction and then in the backward direction. In this tutorial, we will review a branch of Monte Carlo methods based on the forward–backward idea, referred to as backward simulators. These methods are useful for learning and inference in probabilistic models containing latent stochastic processes. The theory and practice of backward simulation algorithms have undergone a significant development in recent years and the algorithms keep finding new applications. The foundation for these methods is sequential Monte Carlo (SMC). SMC-based backward simulators are capable of addressing smoothing problems in sequential latent variable models, such as general, nonlinear/non-Gaussian state-space models (SSMs). However, we will also clearly show that the underlying backward simulation idea is by no means restricted to SSMs. Furthermore, backward simulation plays an important role in recent developments of Markov chain Monte Carlo (MCMC) methods. Particle MCMC is a systematic way of using SMC within MCMC. In this framework, backward simulation gives us a way to significantly improve the performance of the samplers. We review and discuss several related backward-simulation-based methods for state inference as well as learning of static parameters, both using a frequentistic and a Bayesian approach.

Intrinsic profiles beyond the capillary wave theory: a Monte Carlo study.

Abstract: We develop and test an operational definition of the intrinsic surface for liquid-vapor interfaces. The application to the microscopic configurations along Monte Carlo computer simulations gives the statistical properties of the intrinsic surfaces and the intrinsic density profiles for simple fluid models. The results open a framework of quantitative description to close the gap between the mesoscopic capillary wave theory and the sharpest level of resolution for the intrinsic density distribution, relative to the first atomic layer in the liquid surface, as done in the interpretation of experimental x-ray reflectivity.