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Updated: Jul 3, 2025

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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
PubMed
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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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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