Spectroscopic data, particularly diffraction data, contain detailed crystal and microstructure information and thus are crucial for materials discovery. Powder X-ray diffraction (XRD) patterns are greatly effective in identifying crystals. Although machine learning (ML) has significantly advanced the analysis of powder XRD patterns, the progress is hindered by a lack of training data. To address this, we introduce SimXRD, the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics. SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations. We find that the crystal symmetry inherently follows a long-tailed distribution and evaluate 21 sequence learning models on SimXRD. The results indicate that existing neural networks struggle with low-frequency crystal classifications. The present work highlights the academic significance and the engineering novelty of simulated XRD patterns in this interdisciplinary field.
Rust-accelerated powder X-ray diffraction simulation for high-throughput and machine-learning-driven materials science
Faculty of Mechanical Engineering, Czech Technical University in Prague（布拉格捷克理工大学机械工程学院）；Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague（布拉格捷克理工大学核科学与物理工程学院）；FZU – Institute of Physics of the Czech Academy of Sciences（捷克科学院物理研究所）
Emerging hierarchical dislocation structures: Insights from scanning electron microscopy-electron backscatter diffraction in situ tensile testing and multifractal analysis
Cumulative X-ray Damage in Bismuth Selenide Examined by Simultaneous TXM and XRD
Stanford University（斯坦福大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；PULSE Institute（脉冲研究所）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Pohang Accelerator Laboratory（浦项加速器实验室）；Pohang University of Science and Technology（浦项科技大学）；European Radiation Synchrotron Facility（欧洲辐射同步辐射装置）；Nevada National Security Site（内华达国家安全区）；Sogang University（西江大学）
