Affiliation:
1. Ames Laboratory
2. Iowa State University
3. University of Innsbruck
4. Harvard University
Abstract
We present a deep machine learning algorithm to extract crystal field
(CF) Stevens parameters from thermodynamic data of rare-earth magnetic
materials. The algorithm employs a two-dimensional convolutional neural
network (CNN) that is trained on magnetization, magnetic susceptibility
and specific heat data that is calculated theoretically within the
single-ion approximation and further processed using a standard wavelet
transformation. We apply the method to crystal fields of cubic,
hexagonal and tetragonal symmetry and for both integer and half-integer
total angular momentum values JJ
of the ground state multiplet. We evaluate its performance on both
theoretically generated synthetic and previously published experimental
data on CeAgSb_22,
PrAgSb_22
and PrMg_22Cu_99,
and find that it can reliably and accurately extract the CF parameters
for all site symmetries and values of JJ
considered. This demonstrates that CNNs provide an unbiased approach to
extracting CF parameters that avoids tedious multi-parameter fitting
procedures.
Funder
National Science Foundation
United States Department of Energy
Subject
General Physics and Astronomy
Cited by
2 articles.
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