NucleiML is a machine learning surrogate for relativistic mean-field calculations of finite nuclei properties that accelerates Bayesian inference of nuclear equation of state parameters by roughly 1000 times.
The calculation of FN properties, such as bind- ing energy and charge radius for a given set of NMPs and a nucleus AXZ, is formulated as a regression problem
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NucleiML: A machine learning framework of ground-state properties of finite nuclei for accelerated Bayesian exploration
NucleiML is a machine learning surrogate for relativistic mean-field calculations of finite nuclei properties that accelerates Bayesian inference of nuclear equation of state parameters by roughly 1000 times.