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Related Concept Videos

States of Water01:23

States of Water

Water exists in any one of the three classical states: solid (ice), liquid (water), and gas (steam or water vapor). The state of water depends on i) the intermolecular forces that draw molecules together and ii) the kinetic energy that leads to movements that pull them apart.
Water freezes when the intermolecular forces are greater than the kinetic energy. Unlike most other substances, water is less dense in its solid state than in its liquid state. This is because each water molecule can form...
Freezing Point Depression and Boiling Point Elevation03:12

Freezing Point Depression and Boiling Point Elevation

Boiling Point Elevation
The boiling point of a liquid is the temperature at which its vapor pressure is equal to ambient atmospheric pressure. Since the vapor pressure of a solution is lowered due to the presence of nonvolatile solutes, it stands to reason that the solution’s boiling point will subsequently be increased. Vapor pressure increases with temperature, and so a solution will require a higher temperature than will pure solvent to achieve any given vapor pressure, including one...
Freezing Point Depression and Boiling Point Elevation01:24

Freezing Point Depression and Boiling Point Elevation

When a non-volatile solute is added to a pure solvent, it results in the lowering of the freezing point of the solvent. This phenomenon is called freezing point depression. The extent to which the freezing point is lowered depends on the molality of the solute -the number of moles of solute per kilogram of solvent and the cryoscopic constant of the solvent.From the plot of chemical potential, μ, against temperature, it is evident that the μ of both solid and liquid solvents decrease with...
Typical Model Studies01:30

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Modeling and Similitude01:12

Modeling and Similitude

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Updated: Jul 8, 2026

Near-Infrared Temperature Measurement Technique for Water Surrounding an Induction-heated Small Magnetic Sphere
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Machine learning evaluation of structural descriptors for supercooled water.

Kohei Yoshikawa1, Kokoro Shikata1, Kang Kim2

  • 1Division of Chemical Engineering, Department of Materials Engineering Science, Graduate School of Engineering Science, The University of Osaka, Toyonaka, Japan.

Communications Chemistry
|July 6, 2026
PubMed
Summary
This summary is machine-generated.

Researchers evaluated 16 structural descriptors for supercooled water using artificial intelligence. This AI framework objectively assesses how well these descriptors capture temperature-dependent changes in water

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Last Updated: Jul 8, 2026

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Probing the Structure and Dynamics of Interfacial Water with Scanning Tunneling Microscopy and Spectroscopy

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Area of Science:

  • Physical Chemistry
  • Computational Chemistry
  • Materials Science

Background:

  • Anomalous liquid water behavior, including the liquid-liquid phase transition, is linked to tetrahedral hydrogen-bond networks in supercooled states.
  • Understanding these networks requires accurate structural descriptors of local molecular environments.
  • Existing descriptors are often proposed independently, lacking systematic comparison and objective evaluation.

Purpose of the Study:

  • To objectively assess and compare the effectiveness of 16 previously proposed structural descriptors for liquid water.
  • To determine how well these descriptors capture temperature-dependent structural changes in supercooled water.
  • To establish a data-driven framework for benchmarking structural descriptors.

Main Methods:

  • Evaluation of 16 structural descriptors using a neural-network-based temperature classification framework.
  • Application of an explainable artificial intelligence (XAI) method to identify key structural features driving model predictions.
  • Systematic comparison of descriptor performance in distinguishing temperature-dependent structural variations.

Main Results:

  • The neural network framework provided an objective assessment of descriptor performance.
  • Explainable AI identified specific structural features encoded by different descriptors.
  • The study revealed varying abilities of descriptors to capture temperature-induced structural changes.

Conclusions:

  • A data-driven, AI-powered framework enables objective benchmarking of structural descriptors for liquid water.
  • This approach clarifies how different descriptors represent local structural information.
  • The findings facilitate the selection and development of improved descriptors for studying water's complex behavior.