论文标题
用于从原子对分布功能数据确定空间组的空间组计量模型的鲁棒性测试
Robustness test of the spacegroupMining model for determining space groups from atomic pair distribution function data
论文作者
论文摘要
基于卷积神经网络的机器学习模型已用于从其原子配对分布函数（PDF）中预测晶体结构的空间组。但是，用于训练模型的PDF是使用反映特定实验条件的固定参数计算得出的，并且在给定具有不同选择的PDF时，模型的精度未知。在本文中，我们报告说，当将实验参数的不同选择的PDF应用于$ r_ \ text {max} $，$ q_ \ text {max} $，$ q_ \ q_ \ text {damp} $ {damp} $和atomic nomic imentacement参数。
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 paper, we report that the results of the top-1 accuracy and top-6 accuracy are robust when applied to PDFs of different choices of experimental parameters $r_\text{max}$, $Q_\text{max}$, $Q_\text{damp}$ and atomic displacement parameters.
