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Phase Transitions02:31

Phase Transitions

20.2K
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...
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Phase Diagram01:19

Phase Diagram

6.1K
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).
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Phase Transitions: Melting and Freezing02:39

Phase Transitions: Melting and Freezing

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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...
13.1K
Phase Diagrams02:39

Phase Diagrams

43.6K
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...
43.6K
Phase Transitions: Sublimation and Deposition02:33

Phase Transitions: Sublimation and Deposition

17.9K
Some solids can transition directly into the gaseous state, bypassing the liquid state, via a process known as sublimation. At room temperature and standard pressure, a piece of dry ice (solid CO2) sublimes, appearing to gradually disappear without ever forming any liquid. Snow and ice sublimate at temperatures below the melting point of water, a slow process that may be accelerated by winds and the reduced atmospheric pressures at high altitudes. When solid iodine is warmed, the solid sublimes...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Inferring the Isotropic-Nematic Phase Transition with Generative Machine Learning.

Eric R Beyerle1, Pratyush Tiwary2

  • 1University of Maryland, Institute for Physical Science and Technology, College Park, Maryland 20742, USA.

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Summary
This summary is machine-generated.

Generative machine learning models can learn condensed matter physics. Thermodynamic maps successfully predicted liquid crystal phase transitions, demonstrating AI

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Area of Science:

  • Condensed matter physics
  • Machine learning

Background:

  • Generative machine learning models can learn phase behavior.
  • The Ising model is an example of a system exhibiting phase behavior.

Purpose of the Study:

  • To describe the isotropic-nematic phase transition in Gay-Berne ellipsoids using a score-based modeling procedure.
  • To demonstrate the capability of generative machine learning in inferring physical properties of liquid crystal phase transitions.

Main Methods:

  • Utilized a score-based modeling procedure known as thermodynamic maps.
  • Trained the model on samples from either side of the isotropic-nematic phase transition at a single temperature.

Main Results:

  • The generative machine learning approach effectively inferred the nematic order parameter at intermediate temperatures.
  • Successfully described the isotropic-nematic phase transition in a melt of Gay-Berne ellipsoids.

Conclusions:

  • Score-based generative models can learn the underlying physics of complex phase transitions.
  • This approach shows promise for studying nontrivial liquid crystal phase transitions.