# Extrapolation to infinite model space of no-core shell model calculations using machine learning

arXiv: https://arxiv.org/abs/2511.05061 · HTML: https://mlsanity.com/paper/2511.05061/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 7 Nov 2025.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Deep learning: Extrapolation tool for ab initio nuclear theory](https://mlsanity.com/paper/1810.04009/) (2018)
- [Accelerating an Iterative Eigensolver for Nuclear Structure Configuration Interaction Calculations on GPUs using OpenACC](https://mlsanity.com/paper/2109.00485/) (2021)
- [Accelerating quantum many-body configuration interaction with directives](https://mlsanity.com/paper/2110.10765/) (2021)

## Abstract (arXiv)

An ensemble of neural networks is employed to extrapolate no-core shell model (NCSM) results to infinite model space for light nuclei. We present a review of our neural network extrapolations of the NCSM results obtained with the Daejeon16 NN interaction in different model spaces and with different values of the NCSM basis parameter hbarOmega for energies of nuclear states and root-mean-square (rms) radii of proton, neutron and matter distributions in light nuclei. The method yields convergent predictions with quantifiable uncertainties. Ground-state energies for 6Li, 6He, and the unbound 6Be, as well as the excited (3+,0) and (0+,1) states of 6Li, are obtained within a few hundred keV of experiment. The extrapolated radii of bound states converge well. In contrast, radii of unbound states in 6Be and 6Li do not stabilize.