AI models predict decay modes and half-lives of superheavy nuclei with unprecedented accuracy

Dominant decay mode (left panels) and minimum partial half-lives (right panels) of the decay, decay, + decay, EC, and SF. (a,b) Experimental data of NUBASE2020. (cf) The results predicted by RF; WS4 and UNEDF0 identify sources of predicted energy. In particular, FB is used to replace the energy decay to learn SF. Nuclides, for which the predicted partial half-life is longer than 104 s, marked with a star. Credit: Nuclear Science and Technology (2023). DOI: 10.1007/s41365-023-01354-5

In a study published in the journal of Nuclear Science and Technology, researchers from Sun Yat-sen University have achieved a significant breakthrough in understanding the decay processes of superheavy nuclei. Their pioneering study, which uses a random forest machine learning algorithm, offers novel insights into the decay modes and half-lives of elements beyond oganesson (element 118).

In this research, the team focused on nuclei with a proton number (Z) of 84 or higher and a neutron number (N) of 128 or higher, using semi-empirical formulas to calculate the partial half -lives for various decay modes such as alpha. decay, beta-minus decay, beta-plus decay, electron capture, and spontaneous fission (SF). The accuracy of these calculations is further improved by using the random forest algorithm, an advanced machine learning method that combines various nuclear properties and decay energies.

This approach led to new discoveries in nuclear physics, especially the dominance of alpha decay in neutron-deficient regions and beta-minus decay in neutron-rich areas. The accuracy of the algorithm is remarkable, correctly predicting the dominant mode of decay in 96.9% of the studied nuclei, and it also reveals the existence of a long-lived spontaneous fission island in the southwest of element 298 Fl ( flerovium), highlighting the complex interplay between the fission barrier. and Coulomb repulsion of superheavy elements.

This research marks an important leap in the understanding of superheavy nuclei, especially in predicting their decay modes. The insights gained are important for exploring new elements and the elusive “island of stability” in the heavier region. The study also highlights the importance of more accurate measurements of nuclear mass and decay energy for refining predictions. The team proposed several isotopes for future measurements, which will be instrumental in the development of nuclear research, especially in new facilities such as CAFE2 and SHANS2 in Lanzhou.

In summary, the new application of the random forest algorithm opens new doors in nuclear physics, offering a more accurate and comprehensive understanding of the decay processes of superheavy nuclei, and paves the way for future discoveries in this exciting field.

More information:
Bo-Shuai Cai et al, Random forest-based prediction of decay modes and half-lives of superheavy nuclei, Nuclear Science and Technology (2023). DOI: 10.1007/s41365-023-01354-5

Awarded by the Chinese Academy of Sciences

Citation: AI models predict decay modes and half-lives of superheavy nuclei with unprecedented accuracy (2023, December 19) retrieved on December 21, 2023 from https://phys.org/news /2023-12-ai-decay-modes-half-lives-superheavy.html

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