## Physically Interpretable Machine Learning for nuclear masses

### M. Mumpower, T. M. Sprouse, A. Lovell, A. T. Mohan

Published PRCL 106 021301 (2022)

We present a novel 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 $\sim$20\% of the Atomic Mass Evaluation (AME) and predict the remaining $\sim$80\%. The success of our methodology is exhibited by the unprecedented $\sigma_\textrm{RMS}\sim186$ keV match to data for the training set and $\sigma_\textrm{RMS}\sim316$ keV for the entire AME with $Z \geq 20$. We show that our general methodology can be interpreted using feature importance.

LA-UR-22-21855

## Mail

Matthew Mumpower
Los Alamos National Lab
MS B283
TA-3 Bldg 123
Los Alamos, NM 87545