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24 April 2024 |
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Using a machine learning approach to determine the space group of a structure from the atomic pair distribution function (PDF) | | Chia-Hao Liu
; Yunzhe Tao
; Daniel Hsu
; Qiang Du
; Simon J.L. Billinge
; | | Date: |
2 Feb 2019 | | Abstract: | We present a method for predicting the space-group of a structure given a
calculated or measured atomic pair distribution function (PDF) from that
structure. The method uses a deep learning convolutional neural net (CNN)
trained on more than 100,000 PDFs calculated from structures from the 45 most
heavily represented space groups. In particular, we present a CNN model which
yields a promising result that it correctly identifies the space-group among
the top-6 estimates 91.9~\% of the time. The CNN model also successfully
identifies space groups on 12 out of 15 experimental PDFs. We discuss
interesting aspects of the failed estimates, that indicate that the CNN is
failing in similar ways as conventional indexing algorithms applied to
conventional powder diffraction data. This preliminary success of the CNN model
shows the possibility of model-independent assessment of PDF data on a wide
class of materials. | | Source: | arXiv, 1902.0594 | | Services: | Forum | Review | PDF | Favorites | |
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