# Bridging Theory and Data: Correcting Nuclear Mass Models with Interpretable Machine Learning

arXiv: https://arxiv.org/abs/2603.15203 · HTML: https://mlsanity.com/paper/2603.15203/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 83 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024)
- [Quantified limits of the nuclear landscape](https://mlsanity.com/paper/2001.05924/) (2020)
- [Neutron drip line in the Ca region from Bayesian model averaging](https://mlsanity.com/paper/1901.07632/) (2019)

## Abstract (arXiv)

Nuclear mass prediction is one of the core issues in nuclear physics research, yet it faces the challenge of small-sample datasets with high complexity. This study introduces the Kolmogorov-Arnold Network (KAN) into the refinement of nuclear mass models, proposing an efficient and interpretable solution. By constructing the KAN-WS4 hybrid model, the prediction accuracy is significantly improved (the root mean square error is reduced from 0.3 MeV to 0.16 MeV). Furthermore, leveraging the intrinsic interpretability of KAN, feature importance analysis reveals that the proton number is the most critical factor influencing residuals, indicating potential systematic biases in proton-related terms within existing theoretical models. The method's generality is demonstrated across five mass models. This study shows that KAN provides a novel approach to small-sample, high-complexity scientific problems. Its interpretability facilitates the data-driven discovery of physical laws, promising broad applicability to key nuclear physics issues.