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Phase Diagrams

Phase Diagrams

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...

Phase Diagram

Phase Diagram

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).

Phase Transitions: Vaporization and Condensation

Phase Transitions: Vaporization and Condensation

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...

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Phase Transitions

Phase Transitions

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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Related Experiment Video

Updated: May 9, 2025

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

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

Published on: October 24, 2017

Quantum support vector classifier for phase diagram prediction in quinary systems.

Chandra Chowdhury¹

¹Advanced Materials Laboratory, CSIR-Central Leather Research Institute, Sardar Patel Road, Adyar, Chennai, 600020, India. pc.chandra12@gmail.com.

Materials Horizons

|May 6, 2025

Summary

This summary is machine-generated.

Quantum machine learning (QML) using quantum support vector classifiers (QSVC) accurately predicts material phase diagrams. This advances sustainable materials design by improving predictive accuracy and efficiency over classical methods.

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Last Updated: May 9, 2025

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

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Published on: October 24, 2017

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Area of Science:

Materials Science
Quantum Computing
Computational Materials Design

Background:

Classical machine learning (ML) struggles with complex materials datasets.
Quantum machine learning (QML) offers a novel approach to overcome these limitations.
Accurate phase diagram prediction is crucial for developing advanced materials.

Purpose of the Study:

To apply quantum support vector classifier (QSVC) for predicting phase diagrams.
To evaluate QSVC performance in the Al-Cu-Mg-Si-Zn quinary system.
To demonstrate QML's potential in accelerating materials discovery.

Main Methods:

Utilized a comprehensive dataset from high-throughput CALPHAD calculations.
Employed QSVC with advanced quantum feature transformations and kernel methods.
Compared QSVC performance against classical support vector classifiers (SVC).

Main Results:

QSVC demonstrated significant improvements in predictive accuracy.
QSVC showed enhanced efficiency in phase diagram prediction.
Results highlight QML's superiority over classical SVC for complex materials data.

Conclusions:

QML, specifically QSVC, is a powerful tool for materials phase diagram prediction.
This approach facilitates the design of sophisticated materials for sustainable development.
QML integration promises to revolutionize materials science and industrial innovation.