Scientists at Los Alamos National Laboratory are employing machine learning to predict atomic masses and simulate the r process, a key mechanism in forming heavy elements in the universe. Their work has significantly advanced the understanding of atomic nuclei and nuclear structures, influencing experimental nuclear physics research. Credit: SciTechDaily.
“Many thousands of atomic nuclei that have yet to be measured may exist in nature,” said Matthew Mumpower, a theoretical physicist and co-author on several recent papers detailing atomic masses research. “Machine learning algorithms are very powerful, as they can find complex correlations in data, a result that theoretical nuclear physics models struggle to efficiently produce.
Heavy elements may result from this “nucleosynthesis.” In fact, half of the heavy isotopes up to bismuth and all of thorium and uranium in the universe may have been created by the r process. A Los Alamos simulation of an accretion disk after the collision of two neutron stars. This event generates both light and heavy elements. Credit: Los Alamos National Laboratoryprocess requires theoretical predictions of atomic masses currently beyond experimental reach. The team’s physics-informed machine-learning approach trains a model based on random selection from the Atomic Mass Evaluation, a large database of masses.
“We’ve shown that machine learning atomic masses can open the door to predictions beyond where we have experimental data,” Mumpower said. “The critical piece is that we tell the model to obey the laws of physics. By doing so, we enable physics-based extrapolations. Our results are on par with or outperform contemporary theoretical models and can be immediately updated when new data is available.
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