XGBoost residual learning corrects ETFSI fission barrier heights to experimental values with 0.3-1.2 MeV RMSE and identifies binding energies and proton number as key drivers for inner versus outer barriers.
In these nuclei, the results should therefore be interpreted as model-guided extrapola- tions rather than direct validations
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Machine Learning Insights into Discrepancies Between Theoretical and Experimental Fission Barrier Heights
XGBoost residual learning corrects ETFSI fission barrier heights to experimental values with 0.3-1.2 MeV RMSE and identifies binding energies and proton number as key drivers for inner versus outer barriers.