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

Third Law of Thermodynamics02:38

Third Law of Thermodynamics

18.9K
A pure, perfectly crystalline solid possessing no kinetic energy (that is, at a temperature of absolute zero, 0 K) may be described by a single microstate, as its purity, perfect crystallinity,and complete lack of motion means there is but one possible location for each identical atom or molecule comprising the crystal (W = 1). According to the Boltzmann equation, the entropy of this system is zero.
18.9K
Dynamic Equilibrium02:20

Dynamic Equilibrium

51.6K
A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
51.6K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

2.8K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
2.8K
Phase Transitions02:31

Phase Transitions

19.1K
Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to...
19.1K
States of Matter and Phase Changes00:59

States of Matter and Phase Changes

953
The internal energy of a substance—the total kinetic energy of all its molecules and the potential energy of their associated forces—depends on the strength of the intermolecular forces in the condensed phases and the pressure exerted on the substance. The internal energy of a substance is the highest in the gaseous state, the lowest in the solid state, and intermediate in the liquid state. Phase transitions are caused by changes in physical conditions, such as temperature and...
953
Phase Diagram01:19

Phase Diagram

5.9K
The phase of a given substance depends on the pressure and temperature. Thus, plots of pressure versus temperature showing the phase in each region provide considerable insights into the thermal properties of substances. Such plots are known as phase diagrams. For instance, in the phase diagram for water (Figure 1), the solid curve boundaries between the phases indicate phase transitions (i.e., temperatures and pressures at which the phases coexist).
5.9K

You might also read

Related Articles

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

Sort by
Same author

Setting benchmarks for practical quantum utility of combinatorial optimization.

Nature computational science·2026
Same author

General ab initio framework for electronic-order-induced lattice-dynamics symmetry breaking.

Science advances·2026
Same author

Prethermalization by random multipolar driving on a 78-qubit processor.

Nature·2026
Same author

Overcoming feature scarcity in complex system prediction: An alternative delay embedding.

Chaos (Woodbury, N.Y.)·2025
Same author

Multi-scaling reservoir computing learns noise-induced transitions with Lévy noise.

Chaos (Woodbury, N.Y.)·2025
Same author

Free-energy machine for combinatorial optimization.

Nature computational science·2025
Same journal

The BRCA1-A complex restricts replication fork reversal-dependent DNA repair in ATM deficient cells.

Nature communications·2026
Same journal

Signaling downstream of tumor-stroma interaction regulates mucinous colorectal adenocarcinoma apicobasal polarity.

Nature communications·2026
Same journal

Click-polymerized polyenamine membranes for efficient lithium extraction.

Nature communications·2026
Same journal

Joint trajectories of brain atrophy, white matter hyperintensities and cognition quantify brain maintenance.

Nature communications·2026
Same journal

Proton shuttling at electrochemical interfaces under alkaline hydrogen evolution.

Nature communications·2026
Same journal

metilene<sup>3</sup>: identifying DMRs across multiple conditions with auto-classification.

Nature communications·2026
See all related articles

Related Experiment Video

Updated: Jul 4, 2025

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

8.5K

Learning nonequilibrium statistical mechanics and dynamical phase transitions.

Ying Tang1,2, Jing Liu3,4, Jiang Zhang4,5

  • 1Institute of Fundamental and Frontier Sciences, University of Electronic Sciences and Technology of China, Chengdu, 611731, China. jamestang23@gmail.com.

Nature Communications
|February 6, 2024
PubMed
Summary
This summary is machine-generated.

Researchers developed a machine learning framework to study complex nonequilibrium statistical mechanics. This method efficiently computes dynamical partition functions, enabling the discovery of phase transitions in higher dimensions and beyond steady states.

More Related Videos

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

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

Published on: September 17, 2021

4.5K
Combining Microfluidics and Microrheology to Determine Rheological Properties of Soft Matter during Repeated Phase Transitions
11:38

Combining Microfluidics and Microrheology to Determine Rheological Properties of Soft Matter during Repeated Phase Transitions

Published on: April 19, 2018

8.0K

Related Experiment Videos

Last Updated: Jul 4, 2025

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

8.5K
Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

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

Published on: September 17, 2021

4.5K
Combining Microfluidics and Microrheology to Determine Rheological Properties of Soft Matter during Repeated Phase Transitions
11:38

Combining Microfluidics and Microrheology to Determine Rheological Properties of Soft Matter during Repeated Phase Transitions

Published on: April 19, 2018

8.0K

Area of Science:

  • Statistical Mechanics
  • Machine Learning
  • Computational Physics

Background:

  • Nonequilibrium statistical mechanics describes complex systems far from equilibrium.
  • Existing methods struggle with time evolution beyond steady states and in higher dimensions.
  • Characterizing dynamical phase transitions requires tracking system evolution under control parameters.

Purpose of the Study:

  • To develop a general computational framework for studying the time evolution of nonequilibrium systems.
  • To enable efficient computation of the dynamical partition function for discovering phase transitions.
  • To extend the study of dynamical phase transitions to higher dimensions and arbitrary times.

Main Methods:

  • Leveraged variational autoregressive networks for efficient computation.
  • Developed a general computational framework for time evolution studies.
  • Applied the framework to kinetically constrained models of structural glasses in up to three dimensions.

Main Results:

  • Successfully uncovered the active-inactive phase transition of spin flips.
  • Determined the dynamical phase diagram for the studied models.
  • Identified new scaling relations, demonstrating the framework's capability.

Conclusions:

  • The developed machine learning framework provides an efficient method for studying nonequilibrium systems.
  • The approach enables the discovery of dynamical phase transitions in complex systems and higher dimensions.
  • Highlights the significant potential of machine learning in advancing nonequilibrium statistical mechanics research.