Gradient boosted trees trained on nuclear data predict level density parameters for superheavy elements with reported standard deviations from 0.00035 to 0.73.
Fast and accurate modeling of molecular atomization energies with machine learning
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
nucl-th 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Trees and Islands -- Machine learning approach to nuclear physics
Gradient boosted trees trained on nuclear data predict level density parameters for superheavy elements with reported standard deviations from 0.00035 to 0.73.