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Title: Robustness test of the spacegroupMining model for determining space groups from atomic pair distribution function data
Machine learning models based on convolutional neural networks have been used for predicting space groups of crystal structures from their atomic pair distribution function (PDF). However, the PDFs used to train the model are calculated using a fixed set of parameters that reflect specific experimental conditions, and the accuracy of the model when given PDFs generated with different choices of these parameters is unknown. In this work, the results of the top-1 accuracy and top-6 accuracy are robust when applied to PDFs of different choices of experimental parameters r max , Q max , Q damp and atomic displacement parameters.  more » « less
Award ID(s):
1922234
NSF-PAR ID:
10384138
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Journal of Applied Crystallography
Volume:
55
Issue:
3
ISSN:
1600-5767
Page Range / eLocation ID:
626 to 630
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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