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相关概念视频

The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

42.6K
Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
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Quantum Numbers02:43

Quantum Numbers

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It is said that the energy of an electron in an atom is quantized; that is, it can be equal only to certain specific values and can jump from one energy level to another but not transition smoothly or stay between these levels.
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Molecular Models02:00

Molecular Models

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Network Covalent Solids02:18

Network Covalent Solids

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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
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Electron Orbital Model01:18

Electron Orbital Model

68.0K
Orbitals are the areas outside of the atomic nucleus where electrons are most likely to reside. They are characterized by different energy levels, shapes, and three-dimensional orientations. The location of electrons is described most generally by a shell or principal energy level, then by a subshell within each shell, and finally, by individual orbitals found within the subshells.
The first shell is closest to the nucleus, and it has only one subshell with a single spherical orbital called the...
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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量子图 神经网络模型 搜索材料 搜索材料

Ju-Young Ryu1,2, Eyuel Elala1,2, June-Koo Kevin Rhee1,2

  • 1School of Electrical Engineering & ITRC of Quantum Computing for AI, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.

Materials (Basel, Switzerland)
|June 28, 2023
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概括
此摘要是机器生成的。

量子图形神经网络 (QGNNs) 显示出对预测分子性质的承诺,实现比经典模型更低的测试损失和更快的训练. 这项研究探讨了材料科学应用中的QGNN.

关键词:
搜索材料 搜索材料 搜索材料量子图的神经网络的神经网络.量子机器学习就是量子机器学习.

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科学领域:

  • 量子计算是一种量子计算.
  • 材料科学 是一种材料科学.
  • 计算化学是一种计算化学.

背景情况:

  • 经典图形神经网络 (GNN) 越来越多地用于材料研究.
  • 预测能量差距等分子性质对于发现新材料至关重要.
  • 量子计算为复杂的模拟提供了新的范式.

研究的目的:

  • 介绍一个新的量子图神经网络 (QGNN) 模型.
  • 评估QGNN在预测分子性质方面的表现.
  • 将QGNN与用于材料研究的经典GNN进行比较.

主要方法:

  • 开发了一个QGNN模型,灵感来自古典GNN.
  • 使用了等值对角化单位量子图形电路 (EDU-QGC) 框架.
  • 应用QGNNs来预测小型有机分子的能量差距.

主要成果:

  • 与具有类似可训练变量的经典模型相比,QGNNs的测试损失较低.
  • 在培训期间,QGNNs表现出更快的趋同.
  • 该EDU-QGC框架实现了离散链路功能,并最大限度地减少了量子电路嵌入.

结论:

  • QGNN代表了一个强大的新工具,用于预测分子和材料的化学和物理性质.
  • 拟议的QGNN模型在准确性和培训效率方面比传统方法具有优势.
  • 这项工作为材料科学中量子机器学习的进一步发展提供了基础.