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相关概念视频

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In an atom, the negatively charged electrons are attracted to the positively charged nucleus. In a multielectron atom, electron-electron repulsions are also observed. The attractive and repulsive forces are dependent on the distance between the particles, as well as the sign and magnitude of the charges on the individual particles. When the charges on the particles are opposite, they attract each other. If both particles have the same charge, they repel each other.
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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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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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使用神经密度函数来产生潜在的能量表面.

B Jijila1, V Nirmala2, P Selvarengan3

  • 1Queen Mary's College, Chennai, India.

Journal of molecular modeling
|February 10, 2024
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概括

本研究引入了一种新的方法,使用机器学习密度函数近似法 (ML-DFA) 来生成潜在能量表面. 这项研究展示了DeepMind的首次应用.

关键词:
在 DFT 方面,它是最重要的.机器学习的密度函数.神经网络的神经网络的神经网络潜在能量表面的表面.在 TensorFlow 系统中使用 TensorFlow.

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科学领域:

  • 量子化学 是一个量子化学.
  • 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) 与量子化学的整合正在推进密度函数理论 (DFT).
  • DeepMind的深度学习模型 (DM21) 显示了最先进的性能,但其在量子计算中的应用仍未得到充分探索.
  • 现有的文献缺乏应用DM21用于产生潜在能量表面 (PES) 的研究.

研究的目的:

  • 用机器学习密度函数近似法 (ML-DFA) 证明潜在能量表面 (PES) 的生成.
  • 介绍DeepMind预训练的DM21神经网络的首次应用,用于生成ML-DFA-PES.
  • 分析基于DM21的PES的长距离行为,并将其与已建立的DFT函数和CCSDT进行比较.

主要方法:

  • 在TensorFlow框架内使用预训练的DM21神经网络,从电子密度推断交换相关潜力.
  • 计算各种分子几何形状的自相一致场 (SCF) 能量.
  • 通过对相关坐标绘制SCF能量来生成二维潜在能量表面 (PES).
  • 在开源 Python 代码中使用 PySCF 和 DM21 实现 ML-DFA-PES 方法.

主要成果:

  • 成功生成了ML-DFA-PES用于C4H8,H2O,H2和H2+使用DM21m模型与cc-pVDZ基础集.
  • 分析了生成的PES的远程行为,评估了它们的描述能力.
  • 将基于DM21的PES与从流行的DFT函数 (b3lyp,PW6B95) 和CCSD (T) 中获得的PES进行了比较.

结论:

  • 该研究建立了一种基于ML-DFA的新计算方法,用于生成潜在能量表面.
  • 预训练的DM21神经网络显示出在量子化学中准确的PES计算的前景.
  • 这项工作为ML-DFA-PES生成提供了开源实现,促进了未来的研究.