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相关概念视频

Thermodynamic Potentials01:26

Thermodynamic Potentials

776
Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...
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Temperature and Thermal Equilibrium01:11

Temperature and Thermal Equilibrium

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Heat and temperature are essential concepts for everyone every day. The study of heat and temperature is part of an area of physics known as thermodynamics. It is not always easy to distinguish heat and temperature.
The concept of temperature has evolved from the common concepts of hot and cold. The scientific definition of temperature explains more than just our sense of hot and cold. Temperature is operationally defined as the quantity measured with a thermometer. Furthermore, temperature is...
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Thermosensation01:43

Thermosensation

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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...
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Thermal Sigmatropic Reactions: Overview01:16

Thermal Sigmatropic Reactions: Overview

2.1K
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...
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Maxwell's Thermodynamic Relations01:23

Maxwell's Thermodynamic Relations

2.5K
Maxwell's thermodynamic relations are very useful in solving problems in thermodynamics. Each of Maxwell's relations relates a partial differential between quantities that can be hard to measure experimentally to a partial differential between quantities that can be easily measured. These relations are a set of equations derivable from the symmetry of the second derivatives and the thermodynamic potentials.
All thermodynamic potentials are exact differentials. Therefore, their second-order...
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Thermochemical Equations02:55

Thermochemical Equations

28.2K
For a chemical reaction (the system) carried out at constant pressure – with the only work done caused by expansion or contraction – the enthalpy of reaction (also called the heat of reaction, ΔHrxn) is equal to the heat exchanged with the surroundings (qp).
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相关实验视频

Updated: Jun 4, 2025

Author Spotlight: Advancements in High-Performance Thermoelectric Thin Films Through Radio Frequency Magnetron Sputtering
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热电材料性能 (zT) 用机器学习进行预测.

Nikhil K Barua1, Sangjoon Lee2, Anton O Oliynyk3

  • 1Department of Chemistry, Waterloo Data and Artificial Intelligence Institute and Waterloo Institute for Nanotechnology, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

ACS applied materials & interfaces
|December 17, 2024
PubMed
概括

研究人员开发了一个可解释的机器学习模型来预测热电 (TE) 材料性能. 该模型使用大型实验数据集准确预测功效 (zT) 的数字,有助于TE材料的发现.

关键词:
优点的数字 (zT)机器学习是机器学习.材料信息学 材料信息学房地产预测 房地产预测热电材料是一种热电材料.

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相关实验视频

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算材料科学科学 计算材料科学
  • 机器学习 机器学习

背景情况:

  • 机器和深度学习模型在预测材料特性 (如热电性能) 中表现有前途.
  • 现有的模型通常依赖于各种数据源,包括实验,晶体和DFT数据.
  • 准确预测功绩 (zT) 的数字对于推进 TE 材料至关重要.

研究的目的:

  • 开发一个可解释的机器学习模型来预测TE材料性能.
  • 使用大量实验数据集进行培训和验证.
  • 直接预测复杂的zT属性在广泛的TE材料.

主要方法:

  • 开发了一个可解释的机器学习模型.
  • 在大约16万个数据点的大量实验数据集上训练模型.
  • 在三个独立的测试集上验证了模型的预测准确性.

主要成果:

  • 该模型在预测TE材料性能方面取得了很高的准确性,RMSE值在0.15和0.20之间.
  • 在测试组中,评估系数 (R2) 从0.80到0.67不等.
  • 确定了预测和实验zT值之间的偏差的潜在来源.

结论:

  • 开发的可解释模型有效地预测了热电材料的优点 (zT) 数字.
  • 该研究提供了对影响预测准确性的因素的见解,例如实验变异性.
  • 这项工作是利用实验数据直接预测各种材料的复杂 TE 特性的一大重要步骤.