Physics-informed machine learning applied to the identification of high-pressure elusive phases from spatially resolved X-ray diffraction large datasets
Lucas H. Francisco, Camila M. Araújo, André A. M. C. Silva, Ulisses F. Kaneko, Jairo Fonseca, Guilherme A. Calligaris, Audrey D. Grockowiak, Danusa do Carmo, Ri… 展开作者
Lucas H. Francisco, Camila M. Araújo, André A. M. C. Silva, Ulisses F. Kaneko, Jairo Fonseca, Guilherme A. Calligaris, Audrey D. Grockowiak, Danusa do Carmo, Ricardo D. dos Reis, Narcizo M. Souza-Neto
随着第四代同步加速器设施的近期出现而增强的多技术高分辨率 X 射线成像，能够产生庞大的数据集，对传统分析方法构成挑战。在探测非均匀样品的晶体结构时，这种困难明显体现出来：衍射图谱的数量随成像分辨率迅速增加，使得通过直接人工检查在大量反射集合中识别晶体相变得不可行地具有挑战性。在此，我们开发了一种基于无监督聚类算法的新型分析方法，用于识别衍射空间图谱中的独立相，这使我们能够识别高压氢化铈中的材料分布。通过研究该特定化合物，我们还有助于理解超氢化物之间的合成非均匀性；超氢化物是凝聚态物理中一类重要的超导体，即使对最先进的材料技术而言，其表征也极具挑战性。我们提出的分析框架可以很容易地扩展到任何特征与特定实体（如结构相）相关联的相关曲线集合。
英文摘要：
Multi-technique high resolution X-ray mapping enhanced by the recent advent of 4th generation synchrotron facilities can produce colossal datasets, challenging traditional analysis methods. Such difficulty is clearly materialized when probing crystal structure of inhomogeneous samples, where the number of diffraction patterns quickly increases with map resolution, making the identification of crystal phases within a vast collection of reflections unfeasibly challenging by direct human inspection. Here we develop a novel analysis approach based on unsupervised clustering algorithms for identifying independent phases within a diffraction spatial map, which allowed us to identify the material distribution across a high-pressure cerium hydride. By investigating the specific compound, we also contribute to the understanding of synthesis inhomogeneities among the superhydrides, a prominent superconductor class in condensed matter physics whose characterization is highly challenging even for state-of-the-art materials techniques. The analysis framework we present may be readily extended to any correlated set of curves whose features are tied to specific entities, such as structural phases.
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）
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）
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）
Neutron-Diffraction Measurements of an Antiferromagnetic Semiconducting Phase in the Vicinity of the High-Temperature Superconducting State of K$_x$Fe$_{2-y}$Se$_2$
最近发现的K-Fe-Se高温超导体引起了关于其母体化合物性质的激烈争论。输运、角分辨光电子能谱和STM测量表明，它的母体化合物可能是绝缘的、半导体的甚至是金属的[M. H.方，H.- D.王角H.董志J. Li，C.- M. Feng，J. Chen，and H. Q.袁，欧罗巴。Lett. 94，27009（2011）; F. Chen等人Phys.Rev.X1，021020（2011）;和W. Li等人，物理修订信函109，057003（2012）]。由于与这些不同相关联的磁性基态尚未被识别，磁性和超导性之间的关系也没有完全理解，因此该系统的真正母体化合物仍然难以捉摸。在这里，我们报告的中子衍射实验，揭示了一个半导体反铁磁（AFM）阶段与菱形铁空位秩序。半导体相的磁性顺序与铁磷属元素化物母体化合物的条纹AFM顺序相同。此外，当root 5 * root 5块AFM相与超导性共存时，条纹AFM有序被抑制，这使我们推测新的半导体磁性有序相是该超导体的真正母相。
Physics-Informed Glass-Structure Descriptors for Assessing the Intrinsic Reactivity of Mixed Amorphous-Crystalline Precursors in Alkali-Activated Materials
Hebei University of Technology（河北工业大学）；Rice University（莱斯大学）；Rice Advanced Materials Institute, Rice University（莱斯大学先进材料研究所）；Ken Kennedy Institute, Rice University（莱斯大学肯·肯尼迪研究所）
Accelerating dynamic simulations of photoexcited materials and their evolution by electron-informed machine learning
Beijing National Laboratory for Condensed Matter Physics and 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）；School of Physical Sciences, University of Chinese Academy of Sciences（中国科学院大学物理科学学院）；Songshan Lake Materials Laboratory（松山湖材料实验室）
Enhanced Confocal Laser Scanning Microscopy with Adaptive Physics Informed Deep Autoencoders
Georgia State University（佐治亚州立大学）；Riphah International University（里法国际大学）；Roots IVY（鲁茨艾维）；Arid Agriculture University（干旱农业大学）；University of Central Florida（中佛罗里达大学）
Tilted and Twisted Magnetic Moments in the Kitaev Magnet $α$-RuCl$_3$
Institute of High Energy Physics, Chinese Academy of Sciences (CAS)（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）；Jülich Centre for Neutron Science JCNS at MLZ, Forschungszentrum Jülich（于利希研究中心MLZ的JCNS中子科学中心）；II. Physikalisches Institut, Universität zu Köln（科隆大学第二物理研究所）；Jülich Centre for Neutron Science (JCNS) at ILL, Forschungszentrum Jülich（法国国家同步辐射装置ILL的JCNS中子科学中心，于利希研究中心）；Institut für Kristallographie, RWTH Aachen（亚琛工业大学晶体学研究所）；Jülich Centre of Neutron Science at the Heinz Maier-Leibnitz Zentrum（海因茨·迈尔-莱布尼茨中心的于利希中子科学中心）
On-Demand Growth of Semiconductor Heterostructures Guided by Physics-Informed Machine Learning
Institute of Semiconductors, Chinese Academy of Sciences（中国科学院半导体研究所）；University of Chinese Academy of Sciences（中国科学院大学）；HS Photonics Co., Ltd.（华芯光电有限公司）；Zhejiang University（浙江大学）
On Open and Strong-Scaling Tools for Atom Probe Crystallography: High-Throughput Methods for Indexing Crystal Structure and Orientation
Max-Planck-Institut für Eisenforschung GmbH（马克斯·普朗克钢铁研究所）；The University of Alabama（阿拉巴马大学）；Imperial College London（伦敦帝国理工学院）；The University of Sydney（悉尼大学）
