Sentence examples for sampling of configuration from inspiring English sources

Both sets of force fields were used within energy minimizations and molecular dynamics (MD) simulations.[18] The main difference between these two methods is that energy minimizations occur at 0 K and result in a local energy minimum structure; whereas MD simulations include temperature and time and allow for a larger sampling of configuration space.

Alternatively 2), Wallace and De Yoreo employed parallel replica simulations for an extensive sampling of configurations, thus mimicking infinite relaxation times.[ 21] Between these extremes, we suggest 3) a simulated-annealing-type procedure to allow aggregate relaxation to a degree that both energy profiles and aggregate structures evolve continuously as functions of nucleus size.[ 16].

Such methodology has been applied to produce a reduced parameterized model, designed to estimate life-cycle greenhouse gas (GHG) emissions of EGS power plants applicable to a large sample of configurations.

An exact enumeration of walks is impossible and an approximation scheme has to be used by which an unbiased sample of configurations can be generated.

Future work could be focused on further refinement of the structural model reported here, for example by using higher fidelity atomistic force field models, or perhaps by using methods that facilitate the enhanced sampling of structural configurations.

The use of other methods, such as replica exchange simulations or coarse-grained approaches could be explored to provide an additional comparison with the results obtained here, for example to reach a better sampling of structural configurations at long time-scales.

Given a sampling of the configurations after the clustering phase, we learn parameters by maximizing the log likelihood where θ={λ1, λ2, λ3} is the set of parameters.

For each ageing time, seven samples of each configuration were prepared.

A homogeneous sample of square configuration undergoes bending, which is a pure non-rigid deformation, and the deformations are calculated by finite element method, as shown in Fig. 5a where a grid is drawn for visualisation of the deformation.

In this paper, we build and explore supervised learning models of ferromagnetic system behavior, using Monte-Carlo sampling of the spin configuration space generated by the 2D Ising model.

Inspired by metadynamics, it uses several collective variables that are relevant for the investigated process and a bias potential that discourages the sampling of already visited configurations.