DDNP
DDNP
Peer-reviewed research and scientific contributions from the DDNP team
Our cutting-edge research in ensemble learning for nuclear mass predictions
Ensemble learning algorithms, the gradient boosting and bagging regressors, are employed to correct the residuals of nuclear mass excess for a diverse set of six nuclear mass models. The weighted average of these corrected residuals reduces due to their partial cancellation, yielding a significant improvement in nuclear mass predictions. Our conflated model, which integrates ensemble learning and model averaging (ELMA), achieves a root mean square error of approximately 65 keV, well below the critical threshold of 100 keV, for the complete data set of Atomic Mass Evaluation (AME2020).
Achieved unprecedented accuracy in nuclear mass predictions
Comprehensive database covering the entire nuclear landscape
Novel ensemble learning approach for enhanced predictions
We're continuously working on cutting-edge research in nuclear physics, machine learning applications, and data-driven predictions. Stay tuned for more peer-reviewed publications and scientific contributions.