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Crystalline phase structure is essential for understanding the performance and properties of a material. Therefore, this study identified and quantified the crystalline phase structure of a sample based on the diffraction pattern observed…

Materials Science · Physics 2023-09-27 Ryo Murakami , Yoshitaka Matsushita , Kenji Nagata , Hayaru Shouno , Hideki Yoshikawa

X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine-learning-enabled approach to predict crystallographic dimensionality…

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.…

Artificial Intelligence · Computer Science 2026-05-11 Hanyu Gao , Bin Cao , Yunyue Su , Tong-Yi Zhang , Qiang Liu

Crystal structure prediction for a given chemical composition has long been a challenge in condensed-matter science. We have recently shown that experimental powder X-ray diffraction (XRD) data are helpful in a crystal structure search…

Materials Science · Physics 2023-01-30 Seiji Yoshikawa , Ryuhei Sato , Ryosuke Akashi , Synge Todo , Shinji Tsuneyuki

When a sample's X-ray diffraction pattern (XRD) is measured, the corresponding crystal structure is usually determined by searching for similar XRD patterns in the database. However, if a similar XRD pattern is not found, it is tremendously…

Materials Science · Physics 2023-08-08 Joohwi Lee , Junpei Oba , Nobuko Ohba , Seiji Kajita

Machine learning techniques have successfully been used to extract structural information such as the crystal space group from powder X-ray diffractograms. However, training directly on simulated diffractograms from databases such as the…

Materials Science · Physics 2023-10-12 Henrik Schopmans , Patrick Reiser , Pascal Friederich

Accurately determining the crystallographic structure of a material, organic or inorganic, is a critical primary step in material development and analysis. The most common practices involve analysis of diffraction patterns produced in…

Structure is the most basic and important property of crystalline solids; it determines directly or indirectly most materials characteristics. However, predicting crystal structure of solids remains a formidable and not fully solved…

Materials Science · Physics 2021-01-04 Haotong Liang , Valentin Stanev , A. Gilad Kusne , Ichiro Takeuchi

X-ray diffraction (XRD) is an essential technique to determine a material's crystal structure in high-throughput experimentation, and has recently been incorporated in artificially intelligent agents in autonomous scientific discovery…

The prediction of material structure from chemical composition has been a long-standing challenge in natural science. Although there have been various methodological developments and successes with computer simulations, the prediction of…

Materials Science · Physics 2018-05-23 Naoto Tsujimoto , Daiki Adachi , Ryosuke Akashi , Synge Todo , Shinji Tsuneyuki

Determining the atomic-level structure of crystalline solids is critically important across a wide array of scientific disciplines. The challenges associated with obtaining samples suitable for single-crystal diffraction, coupled with the…

Crystal-structure phase mapping is a core, long-standing challenge in materials science that requires identifying crystal structures, or mixtures thereof, in synthesized materials. Materials science experts excel at solving simple systems…

Scientific researchers frequently use the in situ synchrotron high-energy powder X-ray diffraction (XRD) technique to examine the crystallographic structures of materials in functional devices such as rechargeable battery materials. We…

Powder X-ray diffraction (pXRD) experiments are a cornerstone for materials structure characterization. Despite their widespread application, analyzing pXRD diffractograms still presents a significant challenge to automation and a…

Recent advances in materials discovery have been driven by structure-based models, particularly those using crystal graphs. While effective for computational datasets, these models are impractical for real-world applications where atomic…

Machine Learning · Computer Science 2025-07-03 Jithendaraa Subramanian , Linda Hung , Daniel Schweigert , Santosh Suram , Weike Ye

Materials property predictions have improved from advances in machine learning algorithms, delivering materials discoveries and novel insights through data-driven models of structure-property relationships. Nearly all available models rely…

Materials Science · Physics 2022-04-13 Yiqun Wang , Xiao-Jie Zhang , Fei Xia , Elsa A. Olivetti , Ram Seshadri , James M. Rondinelli

Classifying a crystalline solid's phase using X-ray diffraction (XRD) is a challenging endeavor, first because this is a poorly constrained problem as there are nearly limitless candidate phases to compare against a given experimental…

Applied Physics · Physics 2025-05-15 Kangyu Ji , Fang Sheng , Tianran Liu , Basita Das , Tonio Buonassisi

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…

Machine Learning · Computer Science 2026-04-27 Kaipeng Zheng , Weiran Huang , Wanli Ouyang , Han-Sen Zhong , Yuqiang Li

Prediction of stable crystal structures at given pressure-temperature conditions, based only on the knowledge of the chemical composition, is a central problem of condensed matter physics. This extremely challenging problem is often termed…

Materials Science · Physics 2015-05-20 A. R. Oganov , Y. Ma , A. O. Lyakhov , M. Valle , C. Gatti

The in situ synchrotron high-energy X-ray powder diffraction (XRD) technique is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices (e.g., battery materials) or in complex sample…

Image and Video Processing · Electrical Eng. & Systems 2022-12-16 Howard Yanxon , James Weng , Hannah Parraga , Wenqian Xu , Uta Ruett , Nicholas Schwarz