• Letter
  • Open Access

Physically interpretable machine learning for nuclear masses

M. R. Mumpower, T. M. Sprouse, A. E. Lovell, and A. T. Mohan
Phys. Rev. C 106, L021301 – Published 1 August 2022

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 σrms186 keV match to data for the training set and σrms316 keV for the entire AME with Z20. We show that our general methodology can be interpreted using feature importance.

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  • Received 4 March 2022
  • Revised 10 May 2022
  • Accepted 21 July 2022

DOI:https://doi.org/10.1103/PhysRevC.106.L021301

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Nuclear Physics

Authors & Affiliations

M. R. Mumpower*, T. M. Sprouse, and A. E. Lovell

  • Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

A. T. Mohan

  • Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

Article Text

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Issue

Vol. 106, Iss. 2 — August 2022

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