Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Crystal Density

Crystal Density

The crystal lattice structure of a material allows us to determine how many molecules exist in its unit cell. With this information, alongside the unit-cell parameters - three distance parameters (a, b, c) and three angular parameters (α, β, γ).Density (ρ) = (Z × M) / (a × b × c × NA)where:Z is the number of formula units per unit cellM is the molar mass of the substancea, b, and c are the edge lengths of the unit cellNA is Avogadro’s numberFor a simple cubic lattice, atoms are located only at...

Determination of Crystal Structures

Determination of Crystal Structures

In the late 1800s, the revelation that light extended beyond visible wavelengths led to the discovery of X-rays by Wilhelm Roentgen. Recognized as high-energy electromagnetic radiation with short wavelengths, X-rays prompted exploration into their interaction with crystals. Max von Laue proposed in 1912 that the periodic arrangement of atoms, ions, or molecules in crystals would cause them to diffract X-rays, a hypothesis confirmed through experiments with copper sulfate and zinc sulfide...

Structures of Solids

Structures of Solids

Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...

Metallic Solids

Metallic Solids

Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability. Many...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Molecular and Ionic Solids

Molecular and Ionic Solids

Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
Molecular Solids
Molecular crystalline solids, such as ice, sucrose (table sugar), and iodine, are solids that are composed of neutral molecules as their constituent units. These molecules are held together by weak intermolecular forces such as London dispersion forces, dipole-dipole interactions, or hydrogen bonds, which...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Generalized Path Integral Energy and Heat Capacity Estimators of Quantum Oscillators and Crystals Using Harmonic Mapping.

Journal of chemical theory and computation·2024

Same author

Methodical evaluation of Boyle temperatures using Mayer sampling Monte Carlo with application to polymers in implicit solvent.

The Journal of chemical physics·2024

Same author

Harmonic Oscillator Staging Coordinates for Efficient Path Integral Simulations of Quantum Oscillators and Crystals.

Journal of chemical theory and computation·2024

Same author

Efficient single-run implementation of generalized Einstein relation to compute transport coefficients: A binary-based time sampling.

The Journal of chemical physics·2024

Same author

Origins of the Failure of the Activity Virial Series.

The journal of physical chemistry. B·2023

Same author

Probabilistic computations of virial coefficients of polymeric structures described by rigid configurations of spherical particles: A fundamental extension of the ZENO program.

The Journal of chemical physics·2022

Search research articles

Related Experiment Video

Updated: May 26, 2026

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

Algorithm for constant-pressure Monte Carlo simulation of crystalline solids.

Andrew J Schultz¹, David A Kofke

¹Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, New York 14260-4200, USA. ajs42@buffalo.edu

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|December 21, 2011

Summary

This study introduces a new Monte Carlo simulation method for crystalline solids, enhancing volume change acceptance and speeding up property calculations. The improved method offers faster convergence for simulations, especially with hard potentials.

More Related Videos

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

Published on: October 13, 2022

$Synthesis and Microdiffraction at Extreme Pressures and Temperatures$

Synthesis and Microdiffraction at Extreme Pressures and Temperatures

Published on: October 7, 2013

Related Experiment Videos

Last Updated: May 26, 2026

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

Published on: October 13, 2022

$Synthesis and Microdiffraction at Extreme Pressures and Temperatures$

Synthesis and Microdiffraction at Extreme Pressures and Temperatures

Published on: October 7, 2013

Area of Science:

Computational physics
Materials science
Chemical physics

Background:

Isothermal-isobaric Monte Carlo simulations are crucial for studying crystalline solids.
Conventional algorithms can struggle with volume changes, particularly for systems with hard potentials.
Efficient simulation methods are needed to accurately predict material properties.

Purpose of the Study:

To present an alternative method for isothermal-isobaric Monte Carlo simulations of crystalline solids.
To improve the efficiency and convergence of simulations involving volume changes.
To enhance the acceptance probability of volume changes in simulations.

Main Methods:

Developed a novel coordinate scaling approach based on thermodynamics.
Applied the method to isothermal-isobaric Monte Carlo simulations.
Tested the method on hard spheres, Lennard-Jones spheres, and hard dumbbells.

Main Results:

The new method significantly increases the acceptance probability of volume changes.
Coordinate scaling allows for larger simulation step sizes.
Faster convergence of calculated properties was observed across all tested systems.
The improvement was most pronounced for systems with hard potentials, avoiding overlaps during compression.

Conclusions:

The proposed coordinate scaling method offers a substantial improvement over conventional algorithms for simulating crystalline solids.
This technique enhances simulation efficiency and accuracy, particularly for systems with repulsive interactions.
The method facilitates more reliable predictions of material properties through advanced Monte Carlo simulations.