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<p>We propose an enhanced machine learning method to calculate the ground state of two-body systems. By extending the original method [Phys.&#xa0;Rev.&#xa0;Res. <b>5</b>, 033189 (2023)], the present method enables consideration of the spin and isospin degrees of freedom by employing a non-fully connected deep neural network and unsupervised machine learning technique. The validity of this method is verified by calculating the unique bound state of the deuteron.</p>

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A neural network approach for two-body systems with spin and isospin degrees of freedom

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月10日

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  • Chuan-Xin Wang,
  • Tomoya Naito,
  • Jian Li,
  • Hao-Zhao Liang

摘要

We propose an enhanced machine learning method to calculate the ground state of two-body systems. By extending the original method [Phys. Rev. Res. 5, 033189 (2023)], the present method enables consideration of the spin and isospin degrees of freedom by employing a non-fully connected deep neural network and unsupervised machine learning technique. The validity of this method is verified by calculating the unique bound state of the deuteron.