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

Phase Diagram01:19

Phase Diagram

6.0K
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).
6.0K
Phase Diagrams02:39

Phase Diagrams

42.0K
A phase diagram combines plots of pressure versus temperature for the liquid-gas, solid-liquid, and solid-gas phase-transition equilibria of a substance. These diagrams indicate the physical states that exist under specific conditions of pressure and temperature and also provide the pressure dependence of the phase-transition temperatures (melting points, sublimation points, boiling points). Regions or areas labeled solid, liquid, and gas represent single phases, while lines or curves represent...
42.0K
Phase Transitions02:31

Phase Transitions

19.3K
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...
19.3K
Fermi Level01:18

Fermi Level

675
The Fermi-Dirac function is represented by an S-shaped curve indicating the probability of an energy state being occupied by an electron at a given temperature. The Fermi level is the energy level at which there is a fifty percent chance of finding an electron, and it is positioned between the lower-energy valence band and the higher-energy conduction band.
At absolute zero temperature, electrons fill all energy states up to the Fermi level, leaving upper states empty. As the temperature rises,...
675
States of Matter and Phase Changes00:59

States of Matter and Phase Changes

1.0K
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...
1.0K
Phase Transitions: Melting and Freezing02:39

Phase Transitions: Melting and Freezing

12.5K
Heating a crystalline solid increases the average energy of its atoms, molecules, or ions, and the solid gets hotter. At some point, the added energy becomes large enough to partially overcome the forces holding the molecules or ions of the solid in their fixed positions, and the solid begins the process of transitioning to the liquid state or melting. At this point, the temperature of the solid stops rising, despite the continual input of heat, and it remains constant until all of the solid is...
12.5K

You might also read

Related Articles

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

Sort by
Same author

First Evidence for Mixing-Induced CP Violation in B_{s}^{0}→J/ψϕ(1020) Decays in pp Collisions at sqrt[s]=13  TeV.

Physical review letters·2026
Same author

[Analysis of serious adverse events following immunization in Zhejiang Province from 2015 to 2024].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]·2026
Same author

Measurement of D^{0} Meson Photoproduction in Ultraperipheral Heavy Ion Collisions.

Physical review letters·2026
Same author

Observation of Coherent ϕ(1020) Meson Photoproduction in Ultraperipheral PbPb Collisions at sqrt[s_{NN}]=5.36  TeV.

Physical review letters·2026
Same author

Search for New Physics in Jet Multiplicity Patterns of Multilepton Events at sqrt[s]=13  TeV.

Physical review letters·2025
Same author

[A test-negative study on the protective effectiveness of acellular pertussis vaccine in children aged 2 months to 6 years based on propensity score matching method].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]·2025

Related Experiment Video

Updated: Jul 28, 2025

Phase Diagram Characterization Using Magnetic Beads as Liquid Carriers
12:37

Phase Diagram Characterization Using Magnetic Beads as Liquid Carriers

Published on: September 4, 2015

12.4K

Machine Learning the Phase Diagram of a Strongly Interacting Fermi Gas.

M Link1, K Gao1,2, A Kell1

  • 1Physikalisches Institut, University of Bonn, Wegelerstraße 8, 53115 Bonn, Germany.

Physical Review Letters
|June 2, 2023
PubMed
Summary

We used artificial neural networks to map the phase diagram of fermions, revealing a peak in critical temperature within the Bose-Einstein condensate (BEC) to Cooper pair (BCS) crossover.

More Related Videos

Phase Behavior of Charged Vesicles Under Symmetric and Asymmetric Solution Conditions Monitored with Fluorescence Microscopy
10:08

Phase Behavior of Charged Vesicles Under Symmetric and Asymmetric Solution Conditions Monitored with Fluorescence Microscopy

Published on: October 24, 2017

9.3K
Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving
11:21

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving

Published on: March 30, 2017

7.5K

Related Experiment Videos

Last Updated: Jul 28, 2025

Phase Diagram Characterization Using Magnetic Beads as Liquid Carriers
12:37

Phase Diagram Characterization Using Magnetic Beads as Liquid Carriers

Published on: September 4, 2015

12.4K
Phase Behavior of Charged Vesicles Under Symmetric and Asymmetric Solution Conditions Monitored with Fluorescence Microscopy
10:08

Phase Behavior of Charged Vesicles Under Symmetric and Asymmetric Solution Conditions Monitored with Fluorescence Microscopy

Published on: October 24, 2017

9.3K
Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving
11:21

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving

Published on: March 30, 2017

7.5K

Area of Science:

  • Condensed matter physics
  • Quantum mechanics
  • Artificial intelligence applications

Background:

  • Understanding the Bose-Einstein condensate (BEC) to Cooper pair (BCS) crossover is crucial for studying quantum phases.
  • The momentum distribution of fermions is typically considered featureless for condensed state analysis.

Purpose of the Study:

  • To determine the phase diagram of strongly correlated fermions in the BEC-BCS crossover.
  • To apply artificial neural networks (ANNs) and image recognition to analyze fermion momentum distributions.
  • To measure the critical temperature and its behavior across the crossover.

Main Methods:

  • Utilizing an artificial neural network trained on fermion momentum distributions.
  • Applying advanced image recognition techniques to extract information from momentum data.
  • Backanalyzing the trained ANN to understand its interpretation of physical quantities.

Main Results:

  • The phase diagram of the BEC-BCS crossover was successfully determined.
  • The critical temperature was measured and found to exhibit a maximum on the bosonic side of the crossover.
  • The ANN was shown to interpret physically relevant quantities, validating the approach.

Conclusions:

  • Artificial neural networks offer a powerful tool for analyzing complex quantum systems.
  • The study provides new insights into the BEC-BCS crossover and critical temperature behavior.
  • The methodology demonstrates the potential of AI in uncovering hidden information in seemingly featureless data.