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Chem. Sci., 2023, 14, 14003
DOI: 10.1039/D3SC05081E

Progress and challenges in structural, in situ and operando characterization of single-atom catalysts by X-ray based synchrotron radiation techniques
Yuhang Liu, Xiaozhi Su, Jie Ding, Jing Zhou, Zhen Liu, Xiangjun Wei, Hong Bin Yang and Bin Liu
Chem. Soc. Rev., 2024, 53, 11850
DOI: 10.1039/D3CS00967J

MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functions
Emil T. S. Kjær, Andy S. Anker, Andrea Kirsch, Joakim Lajer, Olivia Aalling-Frederiksen, Simon J. L. Billinge and Kirsten M. Ø. Jensen
Digital Discovery, 2024, 3, 908
DOI: 10.1039/D4DD00001C

Computational approaches for XANES, VtC-XES, and RIXS using linear-response time-dependent density functional theory based methods
Daniel R. Nascimento and Niranjan Govind
Phys. Chem. Chem. Phys., 2022, 24, 14680
DOI: 10.1039/D2CP01132H

On the use of NMR distance measurements for assessing surface site homogeneity
Frédéric A. Perras and Damien B. Culver
Dalton Trans., 2023, 52, 18502
DOI: 10.1039/D3DT03201A

Partial density of states representation for accurate deep neural network predictions of X-ray spectra
Clelia Middleton, Basile F. E. Curchod and Thomas J. Penfold
Phys. Chem. Chem. Phys., 2024, 26, 24477
DOI: 10.1039/D4CP01368A

Bridging the Gap between the X-ray Absorption Spectroscopy and the Computational Catalysis Communities in Heterogeneous Catalysis: A Perspective on the Current and Future Research Directions
Rachita Rana, Fernando D. Vila, Ambarish R. Kulkarni and Simon R. Bare
ACS Catal., 2022, 12, 13813
DOI: 10.1021/acscatal.2c03863

Machine learning-driven image synthesis and analysis applications for inertial confinement fusion (invited)
Bradley T. Wolfe, Pinghan Chu, Nga T. T. Nguyen-Fotiadis, Xinhua Zhang, Mariana Alvarado Alvarez and Zhehui Wang
Review of Scientific Instruments, 2024, 95
DOI: 10.1063/5.0219412

Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X-ray Absorption Spectroscopy
Matthew R. Carbone, Phillip M. Maffettone, Xiaohui Qu, Shinjae Yoo and Deyu Lu
J. Phys. Chem. A, 2024, 128, 1948
DOI: 10.1021/acs.jpca.3c06910

Multicode benchmark on simulated Ti K-edge x-ray absorption spectra of Ti-O compounds
Fanchen Meng, Benedikt Maurer, Fabian Peschel, Sencer Selcuk, Mark Hybertsen, Xiaohui Qu, Christian Vorwerk, Claudia Draxl, John Vinson and Deyu Lu
Phys. Rev. Materials, 2024, 8
DOI: 10.1103/PhysRevMaterials.8.013801

Machine learning and data-driven methods in computational surface and interface science
Lukas Hörmann, Wojciech G. Stark and Reinhard J. Maurer
npj Comput Mater, 2025, 11
DOI: 10.1038/s41524-025-01691-6

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Christopher B. Larsen, Kathryn Ledbetter, Daniel R. Nascimento, Elisa Biasin, Muhammad Qureshi, Stanisław H. Nowak, Dimosthenis Sokaras, Niranjan Govind and Amy A. Cordones
J. Am. Chem. Soc., 2024
DOI: 10.1021/jacs.4c11667

Machine learning for analyses and automation of structural characterization of polymer materials
Shizhao Lu and Arthi Jayaraman
Progress in Polymer Science, 2024, 153, 101828
DOI: 10.1016/j.progpolymsci.2024.101828

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Jing Yu, Ivan Pinto-Huguet, Chao Yue Zhang, Yingtang Zhou, Yaolin Xu, Alen Vizintin, Juan-Jesús Velasco-Vélez, Xueqiang Qi, Xiaobo Pan, Gozde Oney, Annabel Olgo, Katharina Märker, Leonardo M. Da Silva, Yufeng Luo, Yan Lu, Chen Huang, Eneli Härk, Joe Fleming, Pascale Chenevier, Andreu Cabot, Yunfei Bai, Marc Botifoll, Ashley P. Black, Qi An, Tazdin Amietszajew and Jordi Arbiol
ACS Energy Lett., 2024, 9, 6178
DOI: 10.1021/acsenergylett.4c02703

Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization
Yiming Chen, Chi Chen, Inhui Hwang, Michael J. Davis, Wanli Yang, Chengjun Sun, Gi-Hyeok Lee, Dylan McReynolds, Daniel Allan, Juan Marulanda Arias, Shyue Ping Ong and Maria K. Y. Chan
Chem. Mater., 2024, 36, 2304
DOI: 10.1021/acs.chemmater.3c02584

Core-to-core X-ray emission spectra from Wannier based multiplet ligand field theory
Charles A. Cardot, Joshua J. Kas, Jared E. Abramson, John J. Rehr and Gerald T. Seidler
Journal of Electron Spectroscopy and Related Phenomena, 2024, 270, 147419
DOI: 10.1016/j.elspec.2024.147419

Emerging Atomistic Modeling Methods for Heterogeneous Electrocatalysis
Zachary Levell, Jiabo Le, Saerom Yu, Ruoyu Wang, Sudheesh Ethirajan, Rachita Rana, Ambarish Kulkarni, Joaquin Resasco, Deyu Lu, Jun Cheng and Yuanyue Liu
Chem. Rev., 2024, 124, 8620
DOI: 10.1021/acs.chemrev.3c00735

Data analysis in spectroscopic STXM
Matthew A. Marcus
Journal of Electron Spectroscopy and Related Phenomena, 2023, 264, 147310
DOI: 10.1016/j.elspec.2023.147310

Sulfur X-ray Absorption and Emission Spectroscopy of Organic Sulfones
Linda I. Vogt, Julien J. H. Cotelesage, Natalia V. Dolgova, Curtis Boyes, Muhammad Qureshi, Dimosthenis Sokaras, Samin Sharifi, Simon J. George, Ingrid J. Pickering and Graham N. George
J. Phys. Chem. A, 2023, 127, 3692
DOI: 10.1021/acs.jpca.2c08647

Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy
Thomas Penfold, Luke Watson, Clelia Middleton, Tudur David, Sneha Verma, Thomas Pope, Julia Kaczmarek and Conor Rankine
Mach. Learn.: Sci. Technol., 2024, 5, 021001
DOI: 10.1088/2632-2153/ad5074

A deep neural network for valence-to-core X-ray emission spectroscopy
T. J. Penfold and C. D. Rankine
Molecular Physics, 2023, 121
DOI: 10.1080/00268976.2022.2123406

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Oleg A. Usoltsev, Aram L. Bugaev, Alexander A. Guda, Sergey A. Guda and Alexander V. Soldatov
J. Phys. Chem. C, 2022, 126, 4921
DOI: 10.1021/acs.jpcc.1c09420

Speciation of Nanocatalysts Using X-ray Absorption Spectroscopy Assisted by Machine Learning
Prahlad K. Routh, Nicholas Marcella and Anatoly I. Frenkel
J. Phys. Chem. C, 2023, 127, 5653
DOI: 10.1021/acs.jpcc.3c00571

Informed Chemical Classification of Organophosphorus Compounds via Unsupervised Machine Learning of X-ray Absorption Spectroscopy and X-ray Emission Spectroscopy
Samantha Tetef, Vikram Kashyap, William M. Holden, Alexandra Velian, Niranjan Govind and Gerald T. Seidler
J. Phys. Chem. A, 2022, 126, 4862
DOI: 10.1021/acs.jpca.2c03635

Importance of feature construction in machine learning for phase transitions
Inhyuk Jang, Supreet Kaur and Arun Yethiraj
The Journal of Chemical Physics, 2022, 157
DOI: 10.1063/5.0102187

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Aaron R. Flanagan, Dhairya Dalal and Frank G. Glavin
Chem. Rev., 2025, 125, 6130
DOI: 10.1021/acs.chemrev.4c00815

Manifold projection image segmentation for nano-XANES imaging
Samantha Tetef, Ajith Pattammattel, Yong S. Chu, Maria K. Y. Chan and Gerald T. Seidler
APL Machine Learning, 2023, 1
DOI: 10.1063/5.0167584

Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning
Jonathan D. Hollenbach, Cassandra M. Pate, Haili Jia, James L. Hart, Paulette Clancy and Mitra L. Taheri
Sci Rep, 2024, 14
DOI: 10.1038/s41598-024-66902-4

Decoding structure-spectrum relationships with physically organized latent spaces
Zhu Liang, Matthew R. Carbone, Wei Chen, Fanchen Meng, Eli Stavitski, Deyu Lu, Mark S. Hybertsen and Xiaohui Qu
Phys. Rev. Materials, 2023, 7
DOI: 10.1103/PhysRevMaterials.7.053802

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?
Miu Lun Lau, Abraham Burleigh, Jeff Terry and Min Long
Journal of Vacuum Science & Technology A, 2023, 41
DOI: 10.1116/6.0002809

Why is EXAFS for complex concentrated alloys so hard? Challenges and opportunities for measuring ordering with X-ray absorption spectroscopy
Howie Joress, Bruce Ravel, Elaf Anber, Jonathan Hollenbach, Debashish Sur, Jason Hattrick-Simpers, Mitra L. Taheri and Brian DeCost
Matter, 2023, 6, 3763
DOI: 10.1016/j.matt.2023.09.010

CuXASNet: Rapid and accurate prediction of copper L -edge x-ray absorption spectra using machine learning
Samuel P. Gleason, Matthew R. Carbone, Deyu Lu and Jim Ciston
Phys. Rev. Materials, 2025, 9
DOI: 10.1103/xz7f-srky

Interpretable multimodal machine learning analysis of X-ray absorption near-edge spectra and pair distribution functions
Tanaporn Na Narong, Zoe N. Zachko, Steven B. Torrisi and Simon J. L. Billinge
npj Comput Mater, 2025, 11
DOI: 10.1038/s41524-025-01589-3

Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network
C. D. Rankine and T. J. Penfold
The Journal of Chemical Physics, 2022, 156
DOI: 10.1063/5.0087255

Review on Perovskite-Type Compound Using Machine Learning
Taohong Zhang, Xueqiang Guo, Han Zheng, Yun Liu, Aziguli Wulamu, Han Chen, Xuxu Guo and Zhizhuo Zhang
sci adv mater, 2022, 14, 1001
DOI: 10.1166/sam.2022.4302

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Fei Zhan, Haodong Yao, Zhi Geng, Lirong Zheng, Can Yu, Xue Han, Xueqi Song, Shuguang Chen and Haifeng Zhao
J. Phys. Chem. A, 2025, 129, 874
DOI: 10.1021/acs.jpca.4c05119

A Δ-learning strategy for interpretation of spectroscopic observables
Luke Watson, Thomas Pope, Raphael M. Jay, Ambar Banerjee, Philippe Wernet and Thomas J. Penfold
Structural Dynamics, 2023, 10
DOI: 10.1063/4.0000215