Or using Deep Learning Neural Networks

Machine Learning and the Deuteron by Kebble and Rios and Variational Monte Carlo calculations of $A\le 4$ nuclei with an artificial neural-network correlator ansatz by Adams et al.

Adams et al: $$\begin{align} H_{LO} &=-\sum_i \frac{{\vec{\nabla}_i^2}}{2m_N} +\sum_{i < j} {\left(C_1 + C_2\, \vec{\sigma_i}\cdot\vec{\sigma_j}\right) e^{-r_{ij}^2\Lambda^2 / 4 }} \nonumber\\ &+D_0 \sum_{i < j < k} \sum_{\text{cyc}} {e^{-\left(r_{ik}^2+r_{ij}^2\right)\Lambda^2/4}}\,, \tag{17} \end{align}$$

where $m_N$ is the mass of the nucleon, $\vec{\sigma_i}$ is the Pauli matrix acting on nucleon $i$, and $\sum_{\text{cyc}}$ stands for the cyclic permutation of $i$, $j$, and $k$. The low-energy constants $C_1$ and $C_2$ are fit to the deuteron binding energy and to the neutron-neutron scattering length

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