Speaker
Description
Nuclear fission is the fundamental mechanism that determines energy production in nuclear power plants. Predictive fission models are necessary for national security applications and are also key to explain the relative abundances of the elements in the universe. In the first part of this talk, we present a novel approach based on particle-number restoration techniques in the fission fragments to predict the preneutron fragment distributions. We show that we can obtain for the first time a qualitatively good description of the mass and charge yields Y(Z, A) using such microscopic approaches. In addition, our method can also describe fine-structural effects such as the odd-even staggering in the charge yields and the charge-polarization of the fission-fragments distributions. In the second part of this talk, we outline a new machine-learning framework to predict microscopic configurations at different nuclear deformations.
Acknowledgments: This work was supported in part by the NUCLEI SciDAC-4 collaboration DE-SC001822 and was performed under the auspices of the U.S.\ Department of Energy by Lawrence Livermore National Laboratory (LLNL) under Contract DE-AC52-07NA27344. Computing support came from the LLNL Institutional Computing Grand Challenge program. Support was also provided by the LLNL Laboratory Directed Research \& Development (LDRD) 21-ERD-001.
Release number: LLNL-ABS-827074