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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

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

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

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

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 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

In recent years, rapid progress has been made in developing artificial intelligence (AI) and machine learning (ML) methods for x-ray absorption spectroscopy (XAS) analysis. Compared to traditional XAS analysis methods, AI/ML approaches…

Chemical recycling of plastics to its constituent monomers is a promising solution to develop a sustainable circular plastic economy. An in-situ X-ray absorption spectra (XAS) characterization is an important way to understand the…

Materials Science · Physics 2024-10-02 Zhengxing Peng , Antoine Lainé , Ka Chon Ng , Mutian Hua , Brett A. Helms , Miquel B. Salmeron , Cheng Wang

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…

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

Resolving morphological chemical phase transformations at the nanoscale is of vital importance to many scientific and industrial applications across various disciplines. The TXM-XANES imaging technique, by combining full field transmission…

Image and Video Processing · Electrical Eng. & Systems 2022-01-04 Jizhou Li , Bin Chen , Guibin Zan , Guannan Qian , Piero Pianetta , Yijin Liu

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

The proliferation of spectroscopic data across various scientific and engineering fields necessitates automated processing. We introduce OASIS (Omni-purpose Analysis of Spectra via Intelligent Systems), a machine learning (ML) framework for…

Machine Learning · Computer Science 2025-09-16 Chris Young , Juejing Liu , Marie L. Mortensen , Yifu Feng , Elizabeth Li , Zheming Wang , Xiaofeng Guo , Kevin M. Rosso , Xin Zhang

In this work, we have developed CuXASNet, a dense neural network that predicts simulated Cu L-edge X-ray absorption spectra (XAS) from atomic structures. Featurization of the Cu local environment is performed using a component of M3GNet, a…

Materials Science · Physics 2024-12-05 Samuel P. Gleason , Matthew R. Carbone , Deyu Lu , Jim Ciston

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

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 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…

Resolving transient atomic configurations in non-crystalline or dynamic environments remains a fundamental bottleneck in the physical sciences. While X-ray absorption spectroscopy (XAS) is a premier probe of local structure, inverting…

Materials Science · Physics 2026-03-31 Suyang Zhong , Boying Huang , Pengwei Xu , Fanjie Xu , Yuhao Zhao , Jun Cheng , Fujie Tang , Weinan E , Zhong-Qun Tian

Predicting materials properties from composition or structure is of great interest to the materials science community. Deep learning has recently garnered considerable interest in materials predictive tasks with low model errors when…

Materials Science · Physics 2021-11-01 Chi Chen , Shyue Ping Ong
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