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

Separable Differential Equations

Separable Differential Equations

A separable differential equation is a type of first-order differential equation where the derivative dy/dx can be expressed as a product of two functions: one that depends only on x and another that depends only on y. This allows for the rearrangement of the equation so that all terms involving y are on one side, and all terms involving x are on the other. This process, known as the separation of variables, simplifies the process of solving the equation by enabling the integration of both...

Multimachine Stability

Multimachine Stability

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:

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...

Maxwell-Boltzmann Distribution: Problem Solving

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by

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...

Lagrange Multipliers: Problem Solving

Lagrange Multipliers: Problem Solving

A silo with a cylindrical base, flat bottom, and hemispherical roof is a common design in agricultural and industrial storage due to its structural efficiency and ease of construction. Optimizing its dimensions to maximize storage capacity for a given amount of material—i.e., a fixed surface area—is a classic problem in applied calculus and engineering design. The key parameters are the radius r of the base and the height h of the cylindrical section.The total volume of the silo is obtained by...

You might also read

Related Articles

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

Sort by

Same author

Non-Markovian Dynamic Models Identify Non-Canonical KRAS-VHL Encounter Complex Conformations for Novel PROTAC Design.

JACS Au·2024

Same author

Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods.

Journal of chemical information and modeling·2023

Same author

A Conserved Local Structural Motif Controls the Kinetics of PTP1B Catalysis.

Journal of chemical information and modeling·2023

Same author

Molecular Latent Space Simulators for Distributed and Multimolecular Trajectories.

The journal of physical chemistry. A·2023

Same author

Predicting the structural basis of targeted protein degradation by integrating molecular dynamics simulations with structural mass spectrometry.

Nature communications·2022

Same author

Iterative data-driven forecasting of the transmission and management of SARS-CoV-2/COVID-19 using social interventions at the county-level.

Scientific reports·2022

Search research articles

Related Experiment Videos

A separable shadow Hamiltonian hybrid Monte Carlo method.

Christopher R Sweet¹, Scott S Hampton, Robert D Skeel

¹Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana 46556, USA.

The Journal of Chemical Physics

|November 10, 2009

Summary

This summary is machine-generated.

The new Separable Shadow Hamiltonian Hybrid Monte Carlo (S2HMC) method improves sampling efficiency for large molecular dynamics systems. S2HMC offers significant speedups over existing methods without requiring additional user parameters.

Related Experiment Videos

Area of Science:

Computational chemistry
Statistical mechanics
Molecular dynamics simulations

Background:

Hybrid Monte Carlo (HMC) is a sampling method using molecular dynamics (MD).
HMC's acceptance rate decreases with increasing system size.
Shadow Hybrid Monte Carlo (SHMC) was developed to mitigate HMC's performance degradation.

Purpose of the Study:

Introduce the Separable Shadow Hamiltonian Hybrid Monte Carlo (S2HMC) method.
Address limitations of SHMC related to generating momenta from nonseparable shadow Hamiltonians.
Improve the efficiency and scalability of Monte Carlo sampling in large systems.

Main Methods:

Developed S2HMC based on a leapfrog/Verlet integrator formulation enabling a separable shadow Hamiltonian.
Efficiently generate momenta from the separable shadow Hamiltonian.
Compare S2HMC performance against HMC and SHMC using numerical experiments.

Main Results:

S2HMC achieves the acceptance rate of a fourth-order integrator with the computational cost of a second-order integrator.
S2HMC demonstrates a speedup greater than two over HMC for systems exceeding 4000 atoms.
SHMC achieved a maximum speedup of only 1.6 over HMC.

Conclusions:

S2HMC significantly enhances sampling efficiency and scalability for large molecular systems.
S2HMC offers superior performance over HMC and SHMC, requiring no additional user parameters.
S2HMC is implemented in PROTOMOL 2.1 and available as a Python version for didactic purposes.