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相关概念视频

Phase Diagram01:19

Phase Diagram

5.9K
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).
5.9K
Phase Diagrams02:39

Phase Diagrams

41.6K
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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相关实验视频

Updated: Jul 20, 2025

Demonstrating the Simplicity and In Situ Temperature Monitoring of the Mechanochemical Synthesis of Metal Chalcogenides Suitable for Thermoelectrics
04:09

Demonstrating the Simplicity and In Situ Temperature Monitoring of the Mechanochemical Synthesis of Metal Chalcogenides Suitable for Thermoelectrics

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使用数据驱动分类器构建的多维相位图进行铜化物的预测合成

Emily M Williamson1, Zhaohong Sun1, Bryce A Tappan1

  • 1Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States.

Journal of the American Chemical Society
|August 4, 2023
PubMed
概括

数据驱动的学习加速了铜化物相位映射. 这种方法使用机器学习来预测材料阶段,从而高精度地高效合成特定的铜化物结构.

科学领域:

  • 材料科学
  • 无机化学
  • 晶体学

背景情况:

  • 铜化物是各种用途的重要材料.
  • 铜-相图复杂,具有多个稳定和转移稳定的晶体结构.
  • 由于这种复杂性,合成控制铜化物相对具有挑战性.

研究的目的:

  • 展示数据驱动的学习来绘制复杂的铜化物相空间.
  • 用最少的实验开发一个预测模型来确定铜化物阶段.
  • 为了实现特定的化铜相的加速和向合成.

主要方法:

  • 使用软化学合成方法.
  • 使用多变量分析和分类技术进行预测阶段确定.
  • 使用四个变量的实验数据构建了一个替代模型:前体结合强度,时间,温度和溶剂组成.

主要成果:

  • 在替代模型中生成了11个不同的铜化物相组合.
  • 训练了一个分类模型,在阶段预测中达到95.7%的准确性.
  • 开发了一个决定树模型,确定影响阶段形成的关键实验变量.

结论:

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  • 数据驱动的学习有效地映射了复杂的材料阶段空间.
  • 预测模型为目标相隔离提供了规范性的合成条件.
  • 成功引导了克洛克曼尼特铜化物 (CuSe) 的加速合成,验证了该方法.