High-precision ab initio nuclear theory: Learning to overcome model-space limitations

TL;DR

This paper reviews how machine learning, especially neural networks, improves the accuracy of nuclear property predictions by extrapolating beyond traditional model-space limitations in ab initio nuclear theory.

Contribution

It compares machine learning extrapolation methods to conventional ones and evaluates their effectiveness for various nuclear observables.

Findings

Machine learning enhances precision in nuclear property predictions.

ML-based extrapolations outperform traditional methods in certain cases.

Uncertainty quantification is improved with statistical and correlation strategies.

Abstract

High-precision predictions of nuclear properties are a central objective of ab initio nuclear structure theory. However, state-of-the-art many-body methods rely on truncated model spaces to render the nuclear many-body problem tractable, which remains a major source of theoretical error in computations of nuclear observables. In recent years, machine learning, and artificial neural network approaches in particular, have emerged as a powerful data-driven framework for learning convergence patterns directly from ab initio calculations and enabling precision extrapolations beyond the reach of conventional schemes. This review focuses on model-space extrapolation methods developed for the no-core shell model and related many-body methods. We discuss machine learning extrapolation frameworks in comparison to conventional methods and assess their performance for energy spectra, radii, and electromagnetic observables, with particular emphasis on achievable precision and uncertainty estimates through statistical and correlation-based strategies. These developments establish machine learning as an increasingly important component of the precision toolbox in ab initio nuclear theory, enhancing the reliability and predictive power of ab initio nuclear structure calculations.