X-ray Diffraction in Crystallography

Últimos artículos

• Inferring Dislocation Microstructures from X-ray Diffraction via Cross-Modal Contrastive Learning

  Benjamin Udofia, Nicolas Bertin, Markus Stricker · 14 de septiembre de 2026

  Understanding and inferring dislocation microstructures from diffraction patterns remains an open challenge in materials characterization, as diffraction measurements provide only indirect information about the underlying dislocation structure. In this work, a cross-modal learning framework is devel…

• Symmetry-Breaking De Novo Crystal Generation via Markovian Jump Diffusion

  Van Khoa Nguyen, Alexandros Kalousis · 14 de agosto de 2026

  Generating crystals has recently attracted significant interest due to their broad applications in materials science. However, existing generative models struggle to produce complete crystallographic specifications, limiting their ability to capture global symmetry and structural dependencies. In pa…

• ED-CSP: Crystal Structure Prediction from Electron Diffraction

  Germain Poloudenny, Ya\"el Fr\'egier, Arnaud Demorti\`ere · 10 de agosto de 2026

  Recovering a periodic 3D crystal structure from sparse, unindexed electron diffraction (ED) observations is a challenging generative inverse problem. Existing ED-based learning methods mainly predict crystallographic labels, reconstruct structures from indexed reflections, or retrieve candidates fro…

• XRDiff: Crystal Structure Prediction from Powder X-Ray Diffraction Data Using Diffusion Models

  Nofit Segal, Mingda Li, Benjamin Kurt Miller, Rafael G\'omez-Bombarelli · 15 de junio de 2026

  Determining the crystal structure of a material from its powder X-ray diffraction (PXRD) pattern is a central challenge in materials science. PXRD is an accessible and widely used characterization technique, yet recovering the atomic structure from diffraction data requires solving an underdetermine…

• XDecomposer: Learning Prior-Free Set Decomposition for Multiphase X-ray Diffraction

  Hanyu Gao, Bin Cao, Yunyue Su, Tong-Yi Zhang, Qiang Liu · 11 de mayo de 2026

  Multiphase powder X-ray diffraction (PXRD) analysis remains a fundamental bottleneck in structure identification, as real-world synthesis often produces complex mixtures whose constituent phases (components) cannot be reliably disentangled. While recent advances in representation-based crystal retri…

• CrystalX: High-accuracy Crystal Structure Analysis Using Deep Learning

  Kaipeng Zheng, Weiran Huang, Wanli Ouyang, Han-Sen Zhong, Yuqiang Li · 27 de abril de 2026

  Atomic structure analysis of crystalline materials is a paramount endeavor in both chemical and material sciences. This sophisticated technique necessitates not only a solid foundation in crystallography but also a profound comprehension of the intricacies of the accompanying software, posing a sign…

• deCIFer: Crystal Structure Prediction from Powder Diffraction Data using Autoregressive Language Models

  Frederik Lizak Johansen, Ulrik Friis-Jensen, Erik Bj{\o}rnager Dam, Kirsten Marie {\O}rnsbjerg Jensen, Roc\'io Mercado, Raghavendra Selvan · 14 de abril de 2026

  Novel materials drive advancements in fields ranging from energy storage to electronics, with crystal structure characterization forming a crucial yet challenging step in materials discovery. In this work, we introduce \emph{deCIFer}, an autoregressive language model designed for powder X-ray diffra…

• Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features

  Mathieu Calvat, Chris Bean, Dhruv Anjaria, Hyoungryul Park, Haoren Wang, Kenneth Vecchio, J. C. Stinville · 9 de febrero de 2026

  To leverage advancements in machine learning for metallic materials design and property prediction, it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-based discrete microstructure descriptors. This need is particularly r…

• CrystalDiT: A Diffusion Transformer for Crystal Generation

  Xiaohan Yi, Guikun Xu, Xi Xiao, Zhong Zhang, Liu Liu, Yatao Bian, Peilin Zhao · 1 de enero de 2026

  We present CrystalDiT, a diffusion transformer for crystal structure generation that achieves state-of-the-art performance by challenging the trend of architectural complexity. Instead of intricate, multi-stream designs, CrystalDiT employs a unified transformer that imposes a powerful inductive bias…