Andreas Edenberg*

Department of Medicine, University of Oslo, Norway

*Corresponding Author: Andreas Edenberg, Department of Medicine, University of Oslo, Norway.

Received: April 25, 2025; Published: May 16, 2025

Abstract

The stability of superheavy elements like Moscovium-299 (Mc-299, Z = 115, A = 299) remains a critical question in nuclear physics. Traditional models, such as the semi-empirical mass formula (SEMF) and the Geiger-Nuttall law, provide foundational estimates of binding energy, alpha decay Q-values, and half-lives [1,2]. However, these models rely on simplified assumptions that may not fully capture the complex nuclear dynamics of superheavy isotopes. This paper explores the integration of artificial intelligence (AI) with these classical methods to enhance predictions of isotope stability. Using a Python-based implementation of SEMF and decay models, we analyze Mc-299’s stability and propose how AI-driven approaches—such as machine learning (ML) and neural networks—could refine these predictions. Results indicate Mc-299’s extreme instability, with a half-life on the order of microseconds, consistent with its superheavy nature. AI’s potential to identify patterns in nuclear data suggests a promising avenue for future research.

Keywords: Superheavy Elements (SHEs);

References

1. Bethe H A and Bacher R F. “Nuclear Physics A. Stationary States of Nuclei”. Reviews of Modern Physics2 (1936): 82-229.
2. Geiger H and Nuttall JM. “The Ranges of α-Particles from Uranium”. Philosophical Magazine129 (1911): 613-621.
3. Krane KS. “Introductory Nuclear Physics”. Wiley (1987).

Citation

Citation: Andreas Edenberg. “Using Artificial Intelligence to Predict Isotope Stability: A Case Study of Moscovium-299".Acta Scientific Computer Sciences 7.3 (2025): 03-05.

Copyright

Copyright: © 2025 Andreas Edenberg. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.