arXiv:2606.06866v1 Announce Type: new Abstract: The prediction of masses of atomic nuclei using machine learning can complement theoretical models and advance the exploration of poorly known domains of the nuclear chart. We propose a machine learning technique based on gated recurrent units (GRU), which have demonstrated competitive performance in nuclear-mass prediction by exploiting long-term dependencies. By integrating multiplicative interactions and product-unit transformations within recurrent units, we report significant improvements in nuclear-mass prediction. Computations are performe
The continuous improvement in machine learning techniques, particularly in areas like recurrent neural networks, enables more precise applications in scientific domains such as nuclear physics.
This development indicates a growing capability for AI to accelerate scientific discovery and complement traditional theoretical models in complex fields, potentially opening new avenues for understanding matter.
The accuracy of nuclear-mass prediction via machine learning significantly improves, offering a more robust tool for nuclear scientists and potentially reducing reliance on costly experimental methods.
- · Nuclear physicists
- · Machine learning researchers
- · Materials science
- · AI compute providers
- · Traditional theoretical modeling (to some extent)
Improved nuclear-mass prediction can guide experiments and research in nuclear energy and materials science.
Enhanced scientific discovery could lead to novel applications in energy generation or material design.
The methodology might be adapted to other complex scientific predictions, accelerating research across various disciplines.
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Read at arXiv cs.LG