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相关概念视频

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Gauss's law helps determine electric fields even though the law is not directly about electric fields but electric flux. In situations with certain symmetries (spherical, cylindrical, or planar) in the charge distribution, the electric field can be deduced based on the knowledge of the electric flux. In these systems, we can find a Gaussian surface S over which the electric field has a constant magnitude. Furthermore, suppose the electric field is parallel (or antiparallel) to the area...
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If a closed surface does not have any charge inside where an electric field line can terminate, then the electric field line entering the surface at one point must necessarily exit at some other point of the surface. Therefore, if a closed surface does not have any charges inside the enclosed volume, then the electric flux through the surface is zero. What happens to the electric flux if there are some charges inside the enclosed volume? Gauss's law gives a quantitative answer to this...
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Consider a polar dielectric placed in an external field. In such a dielectric, opposite charges on adjacent dipoles neutralize each other, such that the net charge within the dielectric is zero. When a polar dielectric is inserted in between the capacitor plates, an electric field is generated due to the presence of net charges near the edge of the dielectric and the metal plates interface. Since the external electrical field merely aligns the dipoles, the dielectric as a whole is neutral. An...
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贝叶斯优化与高斯过程,辅助深度学习,用于材料设计.

Shin Kiyohara1, Yu Kumagai1

  • 1Institute for Materials Research, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan.

The journal of physical chemistry letters
|May 19, 2025
PubMed
概括
此摘要是机器生成的。

深度内核学习 (DKL) 通过将神经网络与高斯过程 (GPs) 结合起来,增强材料发现的贝叶斯优化 (BO). 在探索材料属性方面,DKL表现出比标准GP更高的效率,为更快的材料探索铺平了道路.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学计算化学
  • 机器学习 机器学习

背景情况:

  • 机器学习 (ML) 对于加速材料发现至关重要.
  • 使用高斯过程 (GPs) 的贝叶斯优化 (BO) 是材料探索的一个常见方法.
  • 基于GP的BO效率受到手动特征工程需求的限制.

研究的目的:

  • 调查深度内核学习 (DKL) 与BO结合用于材料发现的有效性.
  • 为了比较基于DKL的BO与基于标准GP的BO的性能.

主要方法:

  • 深度内核学习 (DKL) 的应用,将神经网络与GPs集成到贝叶斯优化 (BO).
  • 在氧化物数据集 (922条目) 上对带间隙,介电常数和电子有效质量进行DKL模型效率的评估.
  • 对混合有机-无机矿合金带间隙 (610条) 的DKL性能评估和4560合金的库里温度预测.

主要成果:

  • 在氧化物和矿数据集上,基于DKL的BO表现出与基于GP的标准BO相比具有可比或更高的效率.
  • 标准GP的表现优于DKL,当可以直接使用与里温度有很强的相关性描述符时.
  • 德克尔的转移学习能力被证明可以进一步提高其效率.

结论:

  • 与深度内核学习增强的贝叶斯优化与标准高斯过程相比,为探索各种材料空间提供了更有效的方法.
  • DKL解决了传统的基于GP的BO的特征工程限制.
  • 基于DKL的BO具有显著的前景,可以加速新材料的发现.