Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

3.1K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
3.1K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.6K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.6K
Randomized Experiments01:13

Randomized Experiments

8.7K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.7K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.2K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.2K
Multimachine Stability01:25

Multimachine Stability

491
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
491
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

226
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...
226

You might also read

Related Articles

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

Sort by
Same author

Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression.

The Journal of chemical physics·2020
See all related articles

Related Experiment Video

Updated: Dec 22, 2025

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

404

Funnel hopping Monte Carlo: An efficient method to overcome broken ergodicity.

Jonas A Finkler1, Stefan Goedecker1

  • 1Department of Physics, University of Basel, Klingelbergstrasse 82, CH-4056 Basel, Switzerland.

The Journal of Chemical Physics
|May 3, 2020
PubMed
Summary

This study introduces Funnel Hopping Monte Carlo (FHMC), an efficient simulation method for atomic systems. FHMC overcomes energy barriers in complex landscapes, significantly reducing simulation time for thermodynamic property investigations.

More Related Videos

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

8.9K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

997

Related Experiment Videos

Last Updated: Dec 22, 2025

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

404
An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

8.9K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

997

Area of Science:

  • Computational physics
  • Statistical mechanics
  • Chemical thermodynamics

Background:

  • Monte Carlo (MC) simulations are vital for studying atomic system thermodynamics.
  • Ergodic sampling is often limited by high energy barriers in configuration space.
  • Existing MC extensions can be computationally expensive or rely on approximations.

Purpose of the Study:

  • To develop a novel, efficient, and exact method for overcoming sampling limitations in MC simulations.
  • To address the challenge of high energy barriers in atomic system simulations.
  • To improve the convergence speed of thermodynamic property investigations.

Main Methods:

  • Proposed Funnel Hopping Monte Carlo (FHMC), inspired by smart darting but more efficient.
  • Utilized Gaussian mixtures to approximate Boltzmann distributions around energy minima.
  • Developed FHMC to facilitate direct jumps between different energy funnels.
  • Integrated FHMC with parallel tempering for enhanced performance.

Main Results:

  • FHMC demonstrated superior efficiency in overcoming energy barriers compared to conventional methods.
  • The method successfully sampled complex energy landscapes, exemplified by Lennard-Jones clusters.
  • FHMC significantly reduced the number of simulation steps required for convergence.
  • Integration with parallel tempering further accelerated the simulation process.

Conclusions:

  • FHMC provides an exact and computationally efficient solution for ergodic sampling challenges in MC simulations.
  • The method is particularly effective for systems with complex, multi-funnel energy landscapes.
  • FHMC offers a significant improvement for investigating thermodynamic properties of atomic systems, reducing computational cost and time.