Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Thermal expansion and Thermal stress: Problem Solving01:27

Thermal expansion and Thermal stress: Problem Solving

1.4K
San Francisco's Golden Gate Bridge is exposed to temperatures ranging from -15 °C to 40 °C. At its coldest, the main span of the bridge is 1275 m long. Assuming that the bridge is made entirely of steel, what is the change in its length between these temperatures?
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in...
1.4K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Family of magnetic field-boosted superconductors in rhombohedral graphene.

Nature·2026
Same author

Toward Hydrogen Isotope Separations through Strong Hydrogen Adsorption at Open Copper(I) Sites in an Ultramicroporous Metal-Organic Framework.

Journal of the American Chemical Society·2026
Same author

Cyclodextrin-Derived Porous Liquids Enabled by In Situ Solvation Shell Formation.

Journal of the American Chemical Society·2026
Same author

Magnetic Anisotropy in the Homoleptic [CoX<sub>4</sub>]<sup>2-</sup> (X = Cl, Br, I) Series: Spectroscopic Determination and Ligand Field Studies.

Inorganic chemistry·2026
Same author

Suppressing thermal transport in nonporous polymer hybrids by limiting thermally accessible vibrational modes.

Materials horizons·2026
Same author

Elucidating the self-assembly of prolamin-derived peptide nanoparticles prepared by enzymatic hydrolysis: implications for loading quercetin and intestinal absorption.

Food chemistry·2026
Same journal

Integrated Electrode-to-Device Design via Combination of Grain Boundary Reconstruction and Dynamic Gas Management Toward Stable 3 Ah Aqueous Zinc-Iodine Pouch Cells.

Advanced materials (Deerfield Beach, Fla.)·2026
Same journal

Diblock Copolymer Engineered Swim Bladder Membrane Enables Spatiotemporal Synchronized Defense and Pro-Healing in Challenging Soft Tissue Regeneration.

Advanced materials (Deerfield Beach, Fla.)·2026
Same journal

Solvation Chemistry Reimagined: LiPF6-Enabled Suppression of Gas Evolution for Ultra-Stable 200 Ah Anode-Free Lithium-Metal Batteries.

Advanced materials (Deerfield Beach, Fla.)·2026
Same journal

Entropy-Driven Conformational Disorder Enables Outstanding High-Temperature Energy Storage in Dielectric Polymers.

Advanced materials (Deerfield Beach, Fla.)·2026
Same journal

Breaking Thermal Conductivity-Electrical Resistivity Trade-Off in Liquid Metal-Based Thermal Interface Materials via Interface Engineering.

Advanced materials (Deerfield Beach, Fla.)·2026
Same journal

Screen-Printed Few-Layer Graphene Platforms for Monitoring Switchable Spin-Crossover Phenomena at Room-Temperature.

Advanced materials (Deerfield Beach, Fla.)·2026
查看所有相关文章

相关实验视频

Updated: Sep 18, 2025

Author Spotlight: Advancements in High-Performance Thermoelectric Thin Films Through Radio Frequency Magnetron Sputtering
04:22

Author Spotlight: Advancements in High-Performance Thermoelectric Thin Films Through Radio Frequency Magnetron Sputtering

Published on: May 17, 2024

3.0K

人工智能驱动的缺陷工程用于先进的热电材料.

Chu-Liang Fu1,2, Mouyang Cheng1,3,4, Nguyen Tuan Hung5

  • 1Quantum Measurement Group, MIT, Cambridge, MA, 02139, USA.

Advanced materials (Deerfield Beach, Fla.)
|June 23, 2025
PubMed
概括
此摘要是机器生成的。

人工智能 (AI) 和机器学习 (ML) 正在通过克服复杂的权衡和缺陷挑战,彻底改变热电材料设计. 这些先进的计算工具加速了用于废热转换的高效热电材料的发现.

关键词:
人工智能的人工智能是人工智能.缺陷工程是什么?缺陷工程是什么?机器学习是机器学习.热电学 热电学 热电学

更多相关视频

Author Spotlight: Advancing Energy Solutions Using Nanocomposites as Processed Thermoelectric Materials
09:23

Author Spotlight: Advancing Energy Solutions Using Nanocomposites as Processed Thermoelectric Materials

Published on: May 17, 2024

1.8K
A Novel Method for In Situ Electromechanical Characterization of Nanoscale Specimens
07:15

A Novel Method for In Situ Electromechanical Characterization of Nanoscale Specimens

Published on: June 2, 2017

9.3K

相关实验视频

Last Updated: Sep 18, 2025

Author Spotlight: Advancements in High-Performance Thermoelectric Thin Films Through Radio Frequency Magnetron Sputtering
04:22

Author Spotlight: Advancements in High-Performance Thermoelectric Thin Films Through Radio Frequency Magnetron Sputtering

Published on: May 17, 2024

3.0K
Author Spotlight: Advancing Energy Solutions Using Nanocomposites as Processed Thermoelectric Materials
09:23

Author Spotlight: Advancing Energy Solutions Using Nanocomposites as Processed Thermoelectric Materials

Published on: May 17, 2024

1.8K
A Novel Method for In Situ Electromechanical Characterization of Nanoscale Specimens
07:15

A Novel Method for In Situ Electromechanical Characterization of Nanoscale Specimens

Published on: June 2, 2017

9.3K

科学领域:

  • 材料科学 材料科学 材料科学
  • 凝聚物质物理学 凝聚物质物理学
  • 计算材料科学科学 计算材料科学

背景情况:

  • 热电材料将废热转化为电力,但性能受到内在性质权衡和缺陷的限制.
  • 发现高性能热电材料是复杂的,因为电导率,Seebeck系数和热导率的相互作用.

研究的目的:

  • 本综述探讨了人工智能 (AI) 和机器学习 (ML) 在推进热电材料设计方面的变革性作用.
  • 突出人工智能驱动的战略,以克服热电材料发现和性能优化方面的挑战.

主要方法:

  • 使用先进的ML模型,如深度神经网络,基于图形的模型和变压器.
  • 集成高通量模拟和广泛的材料数据库,以捕捉复杂的结构-属性关系.
  • 使用人工智能进行缺陷工程,包括组合,,失位和谷物边界优化.

主要成果:

  • 人工智能/ML有效地在复杂的多尺度缺陷空间中导航,克服了材料设计中的"维度诅咒".
  • 人工智能增强的缺陷工程策略被确定用于优化热电特性.
  • 反向设计技术正在出现,用于有针对性的材料属性生成.

结论:

  • 人工智能和机器学习对于加速发现下一代热电材料至关重要.
  • 未来的机遇在于探索新的物理机制,并通过人工智能驱动的设计增强可持续性.
  • 人工智能集成对于克服热电材料科学中长期存在的挑战至关重要.