Machine learning techniques have successfully been used to extract structural information such as the crystal space group from powder X-ray diffractograms. However, training directly on simulated diffractograms from databases such as the ICSD is challenging due to its limited size, class-inhomogeneity, and bias toward certain structure types. We propose an alternative approach of generating synthetic crystals with random coordinates by using the symmetry operations of each space group. Based on this approach, we demonstrate online training of deep ResNet-like models on up to a few million unique on-the-fly generated synthetic diffractograms per hour. For our chosen task of space group classification, we achieved a test accuracy of 79.9% on unseen ICSD structure types from most space groups. This surpasses the 56.1% accuracy of the current state-of-the-art approach of training on ICSD crystals directly. Our results demonstrate that synthetically generated crystals can be used to extract structural information from ICSD powder diffractograms, which makes it possible to apply very large state-of-the-art machine learning models in the area of powder X-ray diffraction. We further show first steps toward applying our methodology to experimental data, where automated XRD data analysis is crucial, especially in high-throughput settings. While we focused on the prediction of the space group, our approach has the potential to be extended to related tasks in the future.

翻译：机器学习技术已被成功应用于从粉末X射线衍射图谱中提取晶体空间群等结构信息。然而，直接利用ICSD等数据库中的模拟衍射图谱进行训练面临诸多挑战，包括数据库规模有限、类别分布不均以及倾向于特定结构类型等。我们提出了一种替代方法：通过利用每个空间群的对称操作，生成具有随机坐标的合成晶体。基于该方法，我们演示了每小时对多达数百万个实时生成的独特合成衍射图谱进行深度类ResNet模型的在线训练。在我们选择的空间群分类任务中，针对来自大多数空间群的未见ICSD结构类型，我们实现了79.9%的测试准确率，超越了当前最先进的直接基于ICSD晶体训练方法56.1%的准确率。我们的结果表明，合成生成的晶体可用于从ICSD粉末衍射图谱中提取结构信息，这使得在粉末X射线衍射领域应用大规模先进机器学习模型成为可能。我们进一步展示了将该方法应用于实验数据的初步步骤——在自动化XRD数据分析至关重要的场景（尤其是高通量环境下）中。尽管本研究聚焦于空间群预测，但该技术未来有望拓展至相关任务领域。