Machine Learning for the Prediction of Converged Energies from Ab Initio Nuclear Structure Calculations

TL;DR

This paper introduces a machine learning approach using neural networks to accurately predict nuclear ground-state energies from ab initio calculations, enabling reliable extrapolation to infinite model spaces across various nuclei.

Contribution

It presents a universal neural network model trained on light nuclei data that can predict energies for larger nuclei, improving extrapolation methods in nuclear structure calculations.

Findings

Neural networks accurately predict energies for 6Li, 12C, and 16O.

The ML approach outperforms classical extrapolation methods.

Model trained on small nuclei generalizes well to larger systems.

Abstract

The prediction of nuclear observables beyond the finite model spaces that are accessible through modern ab initio methods, such as the no-core shell model, pose a challenging task in nuclear structure theory. It requires reliable tools for the extrapolation of observables to infinite many-body Hilbert spaces along with reliable uncertainty estimates. In this work we present a universal machine learning tool capable of capturing observable-specific convergence patterns independent of nucleus and interaction. We show that, once trained on few-body systems, artificial neural networks can produce accurate predictions for a broad range of light nuclei. In particular, we discuss neural-network predictions of ground-state energies from no-core shell model calculations for 6Li, 12C and 16O based on training data for 2H, 3H and 4He and compare them to classical extrapolations.