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The increasing volume and complexity of X-ray absorption spectroscopy (XAS) data generated at synchrotron facilities worldwide require robust infrastructure for data management, sharing, and analysis. This paper introduces the XAS Database…

Databases · Computer Science 2025-09-18 Denis Spasyuk

X-ray absorption spectroscopy (XAS) is a premier technique for materials characterization, providing key information about the local chemical environment of the absorber atom. In this work, we develop a database of sulfur K-edge XAS spectra…

X-ray absorption near edge structure (XANES) spectroscopy is a powerful technique for characterizing the chemical state and symmetry of individual elements within materials, but requires collecting data at many energy points which can be…

Applied Physics · Physics 2025-04-25 Ming Du , Mark Wolfman , Chengjun Sun , Shelly D. Kelly , Mathew J. Cherukara

X-ray absorption spectroscopy is a premier element-specific technique for materials characterization. Specifically, the x-ray absorption near-edge structure (XANES) encodes important information about the local chemical environment of an…

Materials Science · Physics 2019-03-27 Matthew R. Carbone , Shinjae Yoo , Mehmet Topsakal , Deyu Lu

Topological materials discovery has emerged as an important frontier in condensed matter physics. While theoretical classification frameworks have been used to identify thousands of candidate topological materials, experimental…

X-ray absorption spectroscopy (XAS) is a commonly-employed technique for characterizing functional materials. In particular, x-ray absorption near edge spectra (XANES) encodes local coordination and electronic information and machine…

X-ray absorption near edge structure (XANES) is an essential tool for elucidating the atomic-scale, local three-dimensional (3D) structure of given materials and molecules. The rapid computation of XANES based on molecular 3D structures…

Chemical Physics · Physics 2026-02-24 Fei Zhan , Zhi Geng

X-ray absorption spectroscopy (XAS) is a powerful technique to probe the electronic and structural properties of materials. With the rapid growth in both the volume and complexity of XAS datasets driven by advancements in synchrotron…

Computational Physics · Physics 2025-02-25 Xue Han , Haodong Yao , Fei Zhan , Xueqi Song , Junfang Zhao , Haifeng Zhao

X-ray absorption spectroscopy (XAS) is a powerful and well established technique with sensitivity to elemental and chemical composition. Despite these advantages, its implementation has not kept pace with the development of ultrafast pulsed…

X-ray absorption fine structure (XAFS) and x-ray emission spectroscopy (XES) are advanced x-ray spectroscopies that impact a wide range of disciplines. However, unlike the majority of other spectroscopic methods, XAFS and XES are…

This chapter introduces the use of X-ray absorption spectroscopy (XAS) in studying the local electronic and atomic structure of high-entropy materials. The element selectivity of XAS makes it particularly suitable to address the challenges…

Materials Science · Physics 2024-11-12 Alexei Kuzmin

X-ray absorption spectroscopy (XAS) is an element-specific materials characterization technique that is sensitive to structural and electronic properties. First-principles simulated XAS has been widely used as a powerful tool to interpret…

Theoretical simulation is helpful for accurate interpretation of experimental X-ray absorption near-edge structure (XANES) spectra that contain rich atomic and electronic structure information of materials. However, current simulation…

Materials Science · Physics 2026-01-15 Zichang Lin , Wenjie Chen , Yitao Lin , Xinxin Zhang , Yuegang Zhang

The Electron Energy-Loss Spectroscopy (EELS) and X-ray Absorption Spectroscopy (XAS) database has been completely rewritten, with an improved design, user interface and a number of new tools. The database is accessible at https://eelsdb.eu/…

Materials Science · Physics 2016-03-18 Philip Ewels , Thierry Sikora , Virginie Serin , Chris P. Ewels , Luc Lajaunie

Earth-abundant iron is an essential metal in regulating the structure and function of proteins. This study presents the development of a comprehensive X-ray Absorption Spectroscopy (XAS) database focused on iron-containing proteins,…

Biomolecules · Quantitative Biology 2025-09-24 Yufeng Wang , Peiyao Wang , Lu Wei , Emerita Mendoza Rengifo , Dali Yang , Lu Ma , Yuewei Lin , Qun Liu , Haibin Ling

We used interpretable machine learning to combine information from multiple heterogeneous spectra: X-ray absorption near-edge spectra (XANES) and atomic pair distribution functions (PDFs) to extract local structural and chemical…

Materials Science · Physics 2025-04-14 Tanaporn Na Narong , Zoe N. Zachko , Steven B. Torrisi , Simon J. L. Billinge

X-ray Absorption Spectroscopy (XAS) is a powerful technique for probing local atomic environments, yet its interpretation remains limited by the need for expert-driven analysis, computationally expensive simulations, and element-specific…

Machine Learning · Computer Science 2025-08-27 Yufeng Wang , Peiyao Wang , Lu Wei , Lu Ma , Yuewei Lin , Qun Liu , Haibin Ling

Time-resolved X-ray absorption spectroscopy (TR-XAS), based on laser-pump/X-ray probe method, is powerful in capturing the change of geometrical and electronic structure of the absorbing atom upon excitation. TR-XAS data analysis is…

Chemical Physics · Physics 2017-03-08 Fei Zhan , Ye Tao , Haifeng Zhao

Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry and biology. In this work, we present neural network ensemble classifiers for predicting the presence…

Materials Science · Physics 2023-06-29 Matthew R. Carbone , Phillip M. Maffettone , Xiaohui Qu , Shinjae Yoo , Deyu Lu

A deep neural network (DNN) model consisting of two hidden layers was proposed for predicting the immediate environments of specific atoms based on X-ray absorption near-edge spectra (XANES). The output layer of the DNN can be adjusted to…

Computational Physics · Physics 2019-05-13 Liang Li , Mindren Lu , Maria K. Y. Chan
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