Physically interpretable machine learning for nuclear masses
Matthew R. Mumpower, T. M. Sprouse, A. E. Lovell, Arvind Mohan
Abstract
We present an approach to modeling the ground-state mass of atomic nuclei based directly on a probabilistic neural network constrained by relevant physics. Our physically interpretable machine learning (PIML) approach incorporates knowledge of physics by using a physically motivated feature space in addition to a soft physics constraint that is implemented as a penalty to the loss function. We train our PIML model on a random set of approximately 20% of the atomic mass evaluation (AME) and predict the remaining 80%. The success of our methodology is exhibited by a ${\ensuremath{\sigma}}_{\mathrm{rms}}\ensuremath{\approx}186$ keV match to data for the training set and ${\ensuremath{\sigma}}_{\mathrm{rms}}\ensuremath{\approx}316$ keV for the entire AME with $Z\ensuremath{\ge}20$. We show that our general methodology can be interpreted using feature importance.