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Related Concept Videos

Phase Transitions02:31

Phase Transitions

21.8K
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...
21.8K
Phase Transitions: Vaporization and Condensation02:39

Phase Transitions: Vaporization and Condensation

20.0K
The physical form of a substance changes on changing its temperature. For example, raising the temperature of a liquid causes the liquid to vaporize (convert into vapor). The process is called vaporization—a surface phenomenon. Vaporization occurs when the thermal motion of the molecules overcome the intermolecular forces, and the molecules (at the surface) escape into the gaseous state. When a liquid vaporizes in a closed container, gas molecules cannot escape. As these gas phase molecules...
20.0K
Phase Transitions: Sublimation and Deposition02:33

Phase Transitions: Sublimation and Deposition

19.1K
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...
19.1K
Phase Transitions: Melting and Freezing02:39

Phase Transitions: Melting and Freezing

14.1K
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...
14.1K
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.3K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
1.3K

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Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing MTT
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Unsupervised Machine Learning of Quantum Phase Transitions Using Diffusion Maps.

Alexander Lidiak1, Zhexuan Gong1,2

  • 1Department of Physics, Colorado School of Mines, Golden, Colorado 80401, USA.

Physical Review Letters
|December 14, 2020
PubMed
Summary

Unsupervised machine learning can now identify complex quantum phase transitions. The diffusion map method effectively analyzes measurement data from quantum simulators for new physics discovery.

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Area of Science:

  • Quantum physics
  • Machine learning
  • Data analysis

Background:

  • Experimental quantum simulators generate vast datasets, making new physics discovery challenging.
  • Existing unsupervised machine learning methods struggle with complex quantum phase transitions beyond simple order parameters.

Purpose of the Study:

  • To develop and demonstrate an unsupervised machine learning method capable of identifying complex quantum phase transitions.
  • To provide a versatile tool for analyzing data from experimental quantum simulators.

Main Methods:

  • Applied diffusion maps for nonlinear dimensionality reduction and spectral clustering of quantum simulation measurement data.
  • Focused on learning phase transitions involving incommensurate phases, valence-bond solids, topological order, and many-body localization.

Main Results:

  • Diffusion map method shows significant potential for unsupervised learning of complex quantum phase transitions.
  • The method is applicable to measurements of local observables in a single basis.

Conclusions:

  • Diffusion maps offer a promising approach for uncovering new physics in complex quantum systems.
  • This method can be readily applied to diverse experimental quantum simulators.