# Predicting nuclear masses with product-unit networks

arXiv: https://arxiv.org/abs/2305.04675 · HTML: https://mlsanity.com/paper/2305.04675/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 2 of 2)

- [Bayesian approach to model-based extrapolation of nuclear observables](https://mlsanity.com/paper/1806.00552/) (2018)
- [Quantified limits of the nuclear landscape](https://mlsanity.com/paper/2001.05924/) (2020)

## Built on by (most cited first, 4 of 4)

- [PURe: A Plug-and-Play Product-Unit Residual Module for Vision Networks](https://mlsanity.com/paper/2505.04397/) (2025)
- [Modeling Nonlinear Feature Interactions with Product-Unit Residual Networks](https://mlsanity.com/paper/2606.06861/) (2026)
- [Model discovery for dynamical systems with complex-valued product units](https://mlsanity.com/paper/2605.27158/) (2026)
- [Product units in gated recurrent units improve nuclear-mass prediction](https://mlsanity.com/paper/2606.06866/) (2026)

## Abstract (arXiv)

Accurate estimation of nuclear masses and their prediction beyond the experimentally explored domains of the nuclear landscape are crucial to an understanding of the fundamental origin of nuclear properties and to many applications of nuclear science, most notably in quantifying the r-process of stellar nucleosynthesis. Neural networks have been applied with some success to the prediction of nuclear masses, but they are known to have shortcomings in application to extrapolation tasks. In this work, we propose and explore a novel type of neural network for mass prediction in which the usual neuron-like processing units are replaced by complex-valued product units that permit multiplicative couplings of inputs to be learned from the input data. This generalized network model is tested on both interpolation and extrapolation data sets drawn from the Atomic Mass Evaluation. Its performance is compared with that of several neural-network architectures, substantiating its suitability for nuclear mass prediction. Additionally, a prediction-uncertainty measure for such complex-valued networks is proposed that serves to identify regions of expected low prediction error.