<i>AXEAP</i>: a software package for X-ray emission data analysis using unsupervised machine learning

Hwang, Inhui; Solovyev, Mikhail A.; Han, Sang-Wook; Chan, Maria K. Y.; Hammonds, John P.; Heald, Steve M.; Kelly, Shelly D.; Schwarz, Nina; Zhang, Xiaoyi; Sun, Chengjun

doi:10.1107/s1600577522006786

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…Specific to x-ray characterization, ML methods are being used across nearly every characterization technique 30 . Examples include the use of ML to determine the structure-property relationship 31 – 35 , accelerate and enhance coherent characterization techniques 36 – 41 , accelerate emission spectroscopy, reduce dose and noise in tomography, and accelerate Bragg peak fitting 42 – 45 . In the XPCS community, recent work has demonstrated the use of ML to denoise C 2 , which lead to significant improvement of the quantitative interpretation of the XPCS results and detection of anomalous results, and using ML to link physical parameters with C 2 topology from simulations to further our understanding of the origin of complexities in XPCS data 46 – 48 .…”

Section: Introductionmentioning

confidence: 99%

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

Horwath,

Lin,

et al. 2024

Nat Commun

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Understanding and interpreting dynamics of functional materials in situ is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales. However, spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work, we have developed an unsupervised deep learning (DL) framework for automated classification of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system. We demonstrate how this method can be used to accelerate exploration of large datasets to identify samples of interest, and we apply this approach to directly correlate microscopic dynamics with macroscopic properties of a model system. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery.

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Section: Introductionmentioning

confidence: 99%

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

Horwath,

Lin,

et al. 2024

Nat Commun

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“…The focus of ML techniques applied to x-ray spectroscopy has, to date, largely been on the forward mapping problem. Here, in a manner akin to quantum chemistry calculations, an input structure is used to predict binding energies for photoemission [141][142][143], which is converted into the lineshape for XAS [92,110,125,[144][145][146][147][148] or XES [108,129,149]. These methods have addressed light and heavy elements (e.g.…”

Section: Forward Mapping: Structure → Spectrummentioning

confidence: 99%

Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy

Penfold,

Watson,

Middleton

et al. 2024

Mach. Learn.: Sci. Technol.

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Computational spectroscopy has emerged as a critical tool for researchers looking to achieve both qualitative and quantitative interpretations of experimental spectra. Over the past decade, increased interactions between experiment and theory have created a positive feedback loop that has stimulated developments in both domains. In particular, the increased accuracy of calculations has led to them becoming an indispensable tool for the analysis of spectroscopies across the electromagnetic spectrum. This progress is especially well demonstrated for short-wavelength techniques, e.g. core-hole (X-ray) spectroscopies, whose prevalence has increased following the advent of modern X-ray facilities including third-generation synchrotrons and X-ray free-electron lasers (XFELs). While calculations based on well-established wavefunction or density-functional methods continue to dominate the greater part of spectral analyses in the literature, emerging developments in machine-learning algorithms are beginning to open up new opportunities to complement these traditional techniques with fast, accurate, and affordable 'black-box' approaches. This Topical Review recounts recent progress in data-driven/machine-learning approaches for computational X-ray spectroscopy. We discuss the achievements and limitations of the presently-available approaches and review the potential that these techniques have to expand the scope and reach of computational and experimental X-ray spectroscopic studies.

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“…For the 2D-PSD, a DECTRIS 500K was used and placed at a specific distance according to the optical design. More details about the experimental setup, data acquisition and data processing method are given elsewhere (Hwang et al, 2022).…”

Section: Xes Measurement and Experimental Broadening Calculationmentioning

confidence: 99%

“…Our group recently developed a program, Argonne X-ray Emission Analysis Package (AXEAP) (Hwang et al, 2022), that was able to dramatically increase the processing speed of raw data by using unsupervised machine learning. However, data analysis remained challenging due to the tedious and difficult process of finding parameters in multiplet spectral simulations that match experimental data, leading to a trialand-error method for determining them.…”

Section: Introductionmentioning

confidence: 99%

TheAXEAP2program forKβ X-ray emission spectra analysis using artificial intelligence

Hwang,

Kelly,

Chan

et al. 2023

J Synchrotron Rad

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The processing and analysis of synchrotron data can be a complex task, requiring specialized expertise and knowledge. Our previous work addressed the challenge of X-ray emission spectrum (XES) data processing by developing a standalone application using unsupervised machine learning. However, the task of analyzing the processed spectra remains another challenge. Although the non-resonant Kβ XES of 3d transition metals are known to provide electronic structure information such as oxidation and spin state, finding appropriate parameters to match experimental data is a time-consuming and labor-intensive process. Here, a new XES data analysis method based on the genetic algorithm is demonstrated, applying it to Mn, Co and Ni oxides. This approach is also implemented as a standalone application, Argonne X-ray Emission Analysis 2 (AXEAP2), which finds a set of parameters that result in a high-quality fit of the experimental spectrum with minimal intervention. AXEAP2 is able to find a set of parameters that reproduce the experimental spectrum, and provide insights into the 3d electron spin state, 3d–3p electron exchange force and Kβ emission core-hole lifetime.

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AXEAP: a software package for X-ray emission data analysis using unsupervised machine learning

Cited by 5 publications

References 13 publications

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy

TheAXEAP2program forKβ X-ray emission spectra analysis using artificial intelligence

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