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

Real Gases: Effects of Intermolecular Forces and Molecular Volume Deriving Van der Waals Equation04:01

Real Gases: Effects of Intermolecular Forces and Molecular Volume Deriving Van der Waals Equation

34.6K
Thus far, the ideal gas law, PV = nRT, has been applied to a variety of different types of problems, ranging from reaction stoichiometry and empirical and molecular formula problems to determining the density and molar mass of a gas. However, the behavior of a gas is often non-ideal, meaning that the observed relationships between its pressure, volume, and temperature are not accurately described by the gas laws. 
34.6K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

12.9K
The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
12.9K
Calculating Standard Free Energy Changes02:49

Calculating Standard Free Energy Changes

21.2K
The free energy change for a reaction that occurs under the standard conditions of 1 bar pressure and at 298 K is called the standard free energy change. Since free energy is a state function, its value depends only on the conditions of the initial and final states of the system. A convenient and common approach to the calculation of free energy changes for physical and chemical reactions is by use of widely available compilations of standard state thermodynamic data. One method involves the...
21.2K
Chemical Shift: Internal References and Solvent Effects01:17

Chemical Shift: Internal References and Solvent Effects

628
In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
The internal reference compound generally used in NMR spectroscopy is tetramethylsilane (TMS). TMS is preferred because it is chemically inert, soluble in NMR solvents, and easily removable. Also, the highly shielded methyl protons in TMS yield an intense...
628
π Electron Effects on Chemical Shift: Overview01:27

π Electron Effects on Chemical Shift: Overview

1.1K
An applied magnetic field causes loosely bound π-electrons in organic molecules to circulate, producing a local or induced diamagnetic field over a large spatial volume. As the molecules tumble in solution, the field generated by π-electrons in spherical substituents results in a zero net field. However, the net field generated by π-electrons in non-spherical substituents is not zero. The effect of this induced field depends on the orientation of the molecule with respect to B0,...
1.1K
Vapor Pressure02:34

Vapor Pressure

34.8K
When a liquid vaporizes in a closed container, gas molecules cannot escape. As these gas phase molecules move randomly about, they will occasionally collide with the surface of the condensed phase, and in some cases, these collisions will result in the molecules re-entering the condensed phase. The change from the gas phase to the liquid is called condensation. When the rate of condensation becomes equal to the rate of vaporization, neither the amount of the liquid nor the amount of the vapor...
34.8K

You might also read

Related Articles

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

Sort by
Same author

Accurate density functional theory for noncovalent interactions in charged systems.

Science advances·2026
Same author

Machine-Learned Electrostatic Potentials for Accurate Hydration Free Energy Calculations.

Journal of chemical theory and computation·2026
Same author

Acquired Syphilis in a Family Cluster Without Evidence of Sexual Transmission.

Actas dermo-sifiliograficas·2026
Same author

aims-PAX: Parallel Active Exploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials.

Journal of chemical information and modeling·2026
Same author

Correction to "Noncovalent Interactions in Density Functional Theory: All the Charge Density We Do Not See".

Journal of the American Chemical Society·2026
Same author

QMCkl: A kernel library for quantum Monte Carlo applications.

The Journal of chemical physics·2026

Related Experiment Video

Updated: Jun 21, 2025

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

2.2K

Accurate Quantum Monte Carlo Forces for Machine-Learned Force Fields: Ethanol as a Benchmark.

E Slootman1, I Poltavsky2, R Shinde1

  • 1MESA+ Institute for Nanotechnology, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands.

Journal of Chemical Theory and Computation
|July 14, 2024
PubMed
Summary
This summary is machine-generated.

Accurate quantum Monte Carlo (QMC) forces for the fluxional ethanol molecule were obtained using variational or diffusion Monte Carlo methods. Machine-learning force fields trained on these QMC forces accurately reproduced coupled cluster results in molecular dynamics simulations.

More Related Videos

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.8K
Interactive Molecular Model Assembly with 3D Printing
06:15

Interactive Molecular Model Assembly with 3D Printing

Published on: August 13, 2020

10.0K

Related Experiment Videos

Last Updated: Jun 21, 2025

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

2.2K
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.8K
Interactive Molecular Model Assembly with 3D Printing
06:15

Interactive Molecular Model Assembly with 3D Printing

Published on: August 13, 2020

10.0K

Area of Science:

  • Computational chemistry
  • Quantum mechanics
  • Molecular dynamics

Background:

  • Quantum Monte Carlo (QMC) methods are essential for accurate molecular energy and force calculations.
  • The fluxional nature of molecules like ethanol presents challenges for accurate computational modeling.

Purpose of the Study:

  • To obtain accurate forces for the fluxional ethanol molecule at room temperature using QMC methods.
  • To train and evaluate machine-learning force fields based on QMC-derived forces.

Main Methods:

  • Utilizing multideterminant Jastrow-Slater wave functions in variational Monte Carlo (VMC).
  • Employing a single determinant in diffusion Monte Carlo (DMC).
  • Assessing accuracy against high-level coupled cluster (CC) calculations.

Main Results:

  • Accurate QMC forces were obtained for ethanol using both VMC and DMC approaches.
  • Machine-learning force fields trained on DMC forces replicated CC power spectra in molecular dynamics.
  • The study demonstrates the viability of QMC for generating accurate reference data for force field development.

Conclusions:

  • QMC methods, particularly DMC with a single determinant, provide accurate forces for fluxional systems like ethanol.
  • Machine-learning force fields trained on QMC data can achieve high accuracy, comparable to those trained on CC data.
  • This work validates QMC as a reliable source for developing accurate molecular force fields for simulations.