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Metal-Semiconductor Junctions01:24

Metal-Semiconductor Junctions

322
The contact of metal and semiconductor can lead to the formation of a junction with either Schottky or Ohmic behavior.
Schottky Barriers
Schottky barriers arise when a metal with a work function (Φm) contacts a semiconductor with a different work function (Φs). Initially, electrons transfer until the Fermi levels of the metal and semiconductor align at equilibrium. For instance, if Φm > Φs, the semiconductor Fermi level is higher than the metal's before contact. The...
322

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Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses
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增强晶体图卷积神经网络用于预测高度不平衡的数据:金属绝缘体过渡材料的案例研究

Eun Ho Kim1, Jun Hyeong Gu1, June Ho Lee1

  • 1Department of Materials Science and Engineering (MSE), and Division of Advanced Materials Science (AMS), Pohang University of Science and Technology (POSTECH), Pohang 37673, South Korea.

ACS applied materials & interfaces
|August 9, 2024
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概括

机器学习对不平衡的材料科学数据具有挑战性. 深度学习框架Boosting-CGCNN有效地预测少数类金属绝缘体过渡材料,优于其他方法.

关键词:
深度学习是一种深度学习.梯度增强可以提高梯度.图形神经网络 (GNN) 是一个图形神经网络.不平衡的数据不平衡的数据.反向设计的设计.金属绝缘器过渡 (MIT) 的方法

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科学领域:

  • 材料科学 材料科学 材料科学
  • 机器学习 机器学习
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 材料科学中不平衡的数据集对机器学习构成挑战.
  • 像过量采样这样的现有方法可能会导致信息丢失或过度拟合.

研究的目的:

  • 开发一个深度学习框架来预测少数类材料,重点是金属绝缘体过渡 (MIT) 材料.
  • 为了解决材料数据中的极端类不平衡.

主要方法:

  • 引入了增强-CGCNN,将晶体图卷积神经网络 (CGCNN) 与梯度增强相结合.
  • 顺序构建一个更深层的神经网络来处理阶级不平衡.

主要成果:

  • 提升-CGCNN模型有效地处理了麻省理工学院材料数据中的极端阶级不平衡.
  • 通过比较评估,与现有方法相比,表现出优异的性能.

结论:

  • 提升-CGCNN为处理材料科学中不平衡的数据集提供了一个有希望的解决方案.
  • 该框架对于预测像麻省理工学院材料这样的少数类材料特别有效.