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Updated: Jun 17, 2025

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
PubMed
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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Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations13:56

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相关概念视频

Metal-Semiconductor Junctions01:24

Metal-Semiconductor Junctions

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...
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