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Phase Diagram01:19

Phase Diagram

6.9K
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).
6.9K
Phase Diagrams02:39

Phase Diagrams

48.7K
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...
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Classifying Matter by State02:49

Classifying Matter by State

101.9K
Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
101.9K
Heating and Cooling Curves02:44

Heating and Cooling Curves

26.5K
When a substance—isolated from its environment—is subjected to heat changes, corresponding changes in temperature and phase of the substance is observed; this is graphically represented by heating and cooling curves.
For instance, the addition of heat raises the temperature of a solid; the amount of heat absorbed depends on the heat capacity of the solid (q = mcsolidΔT). According to thermochemistry, the relation between the amount of heat absorbed or released by a substance, q, and its...
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Thermosensation01:43

Thermosensation

33.7K
Peripheral thermosensation is the perception of external temperature. A change in temperature (on the surface of the skin and other tissues) is detected by a family of temperature-sensitive ion channels called Transient Receptor Potential, or TRP, receptors. These receptors are located on free nerve endings. Those detecting cold temperatures are closer to the surface of the skin than the nerve endings detecting warmth. These thermoTRP channels, while temperature selective, have relatively...
33.7K
Thermal Sigmatropic Reactions: Overview01:16

Thermal Sigmatropic Reactions: Overview

2.4K
Sigmatropic rearrangements are a class of pericyclic reactions in which a σ bond migrates from one part of a π system to another. These are intramolecular rearrangements where the total number of σ and π bonds remain unchanged.
Sigmatropic shifts are classified based on an order term [i, j ], where i and j indicate the number of atoms across which each end of the σ bond migrates. Below are examples of a [3,3] sigmatropic shift in 1,5-hexadiene, referred...
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Updated: Jan 13, 2026

Asymmetric Thermoelectrochemical Cell for Harvesting Low-grade Heat under Isothermal Operation
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Machine Learning Phase Classification of Thermoelectric Materials.

Chung T Ma1, S Joseph Poon1,2

  • 1Department of Physics, University of Virginia, Charlottesville, VA 22904, USA.

Materials (Basel, Switzerland)
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

This study uses a Support Vector Machine (SVM) model to classify thermoelectric (TE) alloy phases efficiently. This machine learning approach accelerates the discovery of new TE materials with high prediction accuracy.

Keywords:
machine learningphase classificationthermoelectric

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Machine Learning Applications

Background:

  • Thermoelectric (TE) materials are crucial for energy conversion, but exploring their phases is time-consuming and expensive.
  • A vast number of TE alloys exist, necessitating faster methods for phase identification and material discovery.
  • Machine learning (ML) models show promise in predicting material phases, including complex multi-principal element alloys.

Purpose of the Study:

  • To develop and apply a Support Vector Machine (SVM) model for efficient classification of thermoelectric alloy phases.
  • To address the need for time-efficient computational methods in accelerating the discovery of new TE materials.
  • To demonstrate the robustness and accuracy of the ML model in distinguishing between various TE phases.

Main Methods:

  • Implementation of a Support Vector Machine (SVM) classification model.
  • Training and validation of the SVM model on datasets of thermoelectric alloys.
  • Utilizing cross-validation techniques to assess the model's performance across different TE phases.

Main Results:

  • The SVM model achieved high prediction accuracies for TE alloy phase classification, ranging from 77% to 92%.
  • Cross-validation confirmed the model's robustness and reliability in differentiating various thermoelectric phases.
  • The computational approach proved to be time-efficient compared to traditional experimental and ab initio methods.

Conclusions:

  • The developed SVM model offers a computationally efficient method for classifying TE alloy phases.
  • This approach can significantly aid in the evaluation and design of novel, high-performance thermoelectric materials.
  • The study highlights the potential of machine learning to accelerate materials discovery in the field of thermoelectrics.