short communications
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 rmax, Qmax, Qdamp and atomic displacement parameters.
Keywords: robustness testing; machine learning; data mining; space groups; pair distribution functions.
Supporting information
Text file https://doi.org/10.1107/S1600576722002990/jo5075sup1.txt | |
Text file https://doi.org/10.1107/S1600576722002990/jo5075sup2.txt | |
Text file https://doi.org/10.1107/S1600576722002990/jo5075sup3.txt | |
Text file https://doi.org/10.1107/S1600576722002990/jo5075sup4.txt | |
Portable Document Format (PDF) file https://doi.org/10.1107/S1600576722002990/jo5075sup5.pdf |