SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the . . . 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
SimXRD-4M ICLR 2025 - GitHub To benchmark advanced models and further their development, we are launching a Kaggle competition for space group classification Participants are invited to upload their predictions based on the testNOtgt data using their trained models Submit your results on the Leaderboard
SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal Symmetry . . . In this work, we present a novel XRD simulation method (Appendix B 4) that incorporates comprehensive physical interactions Using this method, we developed SimXRD, the largest open-source and physically detailed dataset aimed at advancing this interdisciplinary field
arXiv:1811. 08425v1 [physics. data-an] 20 Nov 2018 ughput material development and discovery loops Specifically, we propose a physics based data augmentation method that extends small, targeted experimental and simulated datasets, and captures the possible differences between simulated XRD powder
opXRD: Open Experimental Powder X-Ray Diffraction Database We invite everyone working with experimental powder XRD to submit any data they would like to publicly share to the dataset, to further improve its utility and thus aid further developments in this field
SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal. . . The authors evaluated 21 sequence models on their XRD dataset, testing both in-library and out-of-library classification scenarios Their analysis revealed that current models struggle with rare crystal types and showed how different physical conditions affect model performance