Materials identification and structural understanding from powder X-ray diffraction (PXRD) data is a long-standing challenge in materials science, fundamental to discovering and characterizing novel materials. A prerequisite for full structure solution is the accurate determination of the crystal lattice, including lattice parameters and crystallographic symmetries. Traditional methods for this are iterative and typically require expert input, and while existing deep learning approaches have shown promise, a robust, single-shot method for comprehensive lattice determination from experimental data remains a key goal. Here, we introduce AlphaDiffract, a deep learning framework that achieves state-of-the-art performance in predicting the crystal system, space group, and lattice parameters directly from PXRD patterns. AlphaDiffract utilizes a 1D adaptation of the ConvNeXt architecture, a modern convolutional neural network that integrates key design principles from transformers, coupled with dedicated prediction heads for each crystallographic property. The model is trained on the largest-to-date physics-based dataset of over 31 million simulated diffraction patterns, generated by augmenting 312,267 curated structures from the ICSD and Materials Project databases. Crucially, it demonstrates strong generalization to experimental data, achieving 81.7% crystal system accuracy and 66.2% space group accuracy on the RRUFF dataset while additionally predicting all six lattice parameters. By providing a unified model for rapid and accurate lattice determination from PXRD data, AlphaDiffract represents a significant step forward in leveraging deep learning for high-throughput materials discovery.
Nanoparticle size distribution estimation by full-pattern powder diffraction analysis
随着科学和技术对纳米粒子的兴趣日益增加，对快速、高效和精确的表征技术的需求也越来越大。粉末衍射是一种非常有效的实验方法，因为它是直接和非破坏性的。然而，它用于提取关于非常小的颗粒的信息，使一些常见的晶体学近似值达到或超过其有效性极限。重点讨论了粉末衍射谱的计算方法，重点讨论了具有对数正态分布的球形颗粒，其目标是确定粒度分布参数。作为实例，分析20-nm CeO $_{2}$样品。
MatDiffract: A Material-Informed Automated Analysis Platform for X-ray Powder Diffraction
Institute of High Energy Physics, Chinese Academy of Sciences（中国科学院高能物理研究所）；Spallation Neutron Source Science Center（散裂中子源科学中心）；College of Computer and Information Engineering, Henan Normal University（河南师范大学计算机与信息工程学院）；Dongguan Institute of Materials Science and Technology, Chinese Academy of Sciences（中国科学院东莞材料科学与技术研究所）；Songshan Lake Material Laboratory（松山湖材料实验室）；School of Computer Science, Sun Yat-sen University（中山大学计算机学院）
Ab-initio Crystal Structure Determination from Powder X-Ray Diffraction
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；North Carolina Battery Complexity, Autonomous Vehicle and Electrification (BATT CAVE) Research Center（北卡罗来纳电池复杂性、自动驾驶与电气化（BATT CAVE）研究中心）
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）
Inhomogeneous Magnetism in La-doped CaMnO3. (II) Mesoscopic Phase Separation due to Lattice-coupled FM Interactions
本文详细研究了Ca（1-x）La（x）MnO_3（0.00 ≤ x ≤ 0.20）的介观磁分离和晶体学相分离。中子粉末衍射和直流磁化技术已被用来隔离所扮演的不同角色的电子掺杂到作为其浓度x的函数的eg水平。多个低温磁性和结晶相内的个别多晶样品的存在被认为是一个内在的功能，遵循从竞争FM和AFM相互作用之间的平衡作为温度的函数的系统。FM双交换相互作用与掺杂的EG电子在较高的温度下比竞争AFM相互作用更有利，并且通过轨道极化与晶格更强地耦合。这些FM相互作用，从而发挥了特权的作用，即使在低例如电子浓度，凭借以上的AFM转变温度诱导的结构修改。
Generative crystallographic phasing through invariant relationships
The Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences（中国科学院物理研究所凝聚态物理国家实验室）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Dept. of Comp. Sci. and Tech., Institute for AI, Tsinghua University（清华大学智能产业研究院计算机科学与技术系）；Institute for AIR, Tsinghua University（清华大学智能产业研究院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE（教育部下一代智能搜索与推荐工程研究中心）；Beijing Key Laboratory of Big Data Management and Analysis Methods（北京大数据管理与分析方法重点实验室）；School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学技术学院）；Zhongguancun Academy（中关村学院）
Crystal growth, characterization and phase transition of PbCuTe$_2$O$_6$
采用移动溶剂浮区法（TSFZ）和顶部籽晶溶液法（TSSG）生长了三维阻挫磁体和自旋液体候选化合物PbCuTe $_2$ O $_6$的单晶。通过研究热性能优化了生长条件。通过偏光显微镜、X射线劳厄和X射线粉末衍射检查晶体的质量，并与多晶样品进行比较。采用TSSG法获得了质量优良的晶体。这些晶体的磁性测量结果显示，与以前报道的数据相比，不同的结晶方向的小的各向异性。单晶和粉末样品的热容都在1K附近出现了相变异常。奇怪的是，转变的位置和幅度强烈依赖于微晶尺寸，并且对于最小的微晶几乎完全不存在。一个结构转变的建议，伴随着报道的铁电转变，并提出了一个方案，使它成为积极不利的小微晶。
Boston College（波士顿学院）；NIST Center for Neutron Research, National Institute of Standards and Technology（美国国家标准与技术研究院中子研究中心）；Carnegie Mellon University（卡内基梅隆大学）；University of California, Berkeley（加州大学伯克利分校）；University of Maryland, College Park（马里兰大学帕克分校）
Crystal structures and magnetic order of La{0.5+delta}A{0.5-delta}Mn{0.5+epsilon}Ru{0.5-epsilon}O{3} (A= Ca, Sr, Ba): Possible orbital glass ferromagnetic state
Effect of Niobium Doping on the Crystal Structure and Hydrogen Sorption Properties of TiFe: Combined Synchrotron X-ray Diffraction and Extended X-ray Absorption Fine Structure Study
University of Stavanger（斯塔万格大学）；Institute for Energy Technology（能源技术研究所）
XDecomposer: Learning Prior-Free Set Decomposition for Multiphase X-ray Diffraction
New Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所新型模式识别实验室）；School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉学科学院）；Guangzhou Municipal Key Laboratory of Materials Informatics, The Hong Kong University of Science and Technology (Guangzhou)（广州市材料信息学重点实验室，香港科技大学（广州））；Green Dynamics, Australia（澳大利亚绿动公司）
