Convolutional Neural Network Analysis of Two-Dimensional Hyperfine Sublevel Correlation Electron Paramagnetic Resonance Spectra

Alexander T. Taguchi, Ethan D. Evans, Sergei A. Dikanov, Robert G. Griffin

Research output: Contribution to journalArticle

Abstract

A machine learning approach is presented for analyzing complex two-dimensional hyperfine sublevel correlation electron paramagnetic resonance (HYSCORE EPR) spectra with the proficiency of an expert spectroscopist. The computer vision algorithm requires no training on experimental data; rather, all of the spin physics required to interpret the spectra are learned from simulations alone. This approach is therefore applicable even when insufficient experimental data exist to train the algorithm. The neural network is demonstrated to be capable of utilizing the full information content of two-dimensional 14 N HYSCORE spectra to predict the magnetic coupling parameters and their underlying probability distributions that were previously inaccessible. The predicted hyperfine (a, T) and 14 N quadrupole (K, η) coupling constants deviate from the previous manual analyses of the experimental spectra on average by 0.11 MHz, 0.09 MHz, 0.19 MHz, and 0.09, respectively.

Original languageEnglish (US)
Pages (from-to)1115-1119
Number of pages5
JournalJournal of Physical Chemistry Letters
Volume10
Issue number5
DOIs
StatePublished - Mar 7 2019

ASJC Scopus subject areas

  • Materials Science(all)
  • Physical and Theoretical Chemistry

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