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RESEARCH PAPER · Physical review. B./Physical review. B · 2021

Determining electronic properties from L -edge x-ray absorption spectra of transition metal compounds with artificial neural networks

Johann Lüder

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Determining electronic properties from L -edge x-ray absorption spectra of transition metal compounds with artificial neural networksOriginalA machine learning approach to predict L-edge x-ray absorption spectra of light transition metal ion compounds3Beyond structural insight: a deep neural network for the prediction of Pt L 2/3 -edge X-ray absorption spectra12Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW69Theory+AI/ML for microscopy and spectroscopy: Challenges and opportunities20Machine‐Learning Spectral Indicators of Topology35Bridging the Gap between the X-ray Absorption Spectroscopy and the Computational Catalysis Communities in Heterogeneous Catalysis: A Perspective on the Current and Future Research Directions30The trials and triumphs of modelling X-ray absorption spectra of transition metal phthalocyanines7Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization28Integrating Machine Learning Potential and X-ray Absorption Spectroscopy for Predicting the Chemical Speciation of Disordered Carbon Nitrides7Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy12How scanning probe microscopy can be supported by artificial intelligence and quantum computing?10Machine learning approach to predict L -edge x-ray absorption spectra of light transition metal ion compounds5Atomic Metal‒Nonmetal Catalytic Pair Cooperatively Drives Efficient Enzyme‐Mimetic Catalysis12Atomic Metal‒Nonmetal Catalytic Pair Cooperatively Drives Efficient Enzyme‐Mimetic Catalysis1Hamiltonian parameter inference from resonant inelastic x-ray scattering with active learning0Single Cu Atom Sites on Co 3 O 4 Activate Interfacial Oxygen for Enhanced Reactivity and Selective Gas Sensing at Low Temperature3Machine-Learning-Assisted X-ray Spectroscopy Decoding an Elemental Segregation Mechanism in Pt–Ru Binary Alloy Nanoparticles0From Local Atomic Structure to X-ray Spectra: Absorber-Centric Machine-Learning Encoding0Precise analysis and proper application of X-ray absorption spectroscopy0Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy0

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  1. A machine learning approach to predict L-edge x-ray absorption spectra of light transition metal ion compounds

    arXiv (Cornell University) · 2021-07-28

    Johann Lüder

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  2. Beyond structural insight: a deep neural network for the prediction of Pt L 2/3 -edge X-ray absorption spectra

    Physical Chemistry Chemical Physics · 2022-01-01

    Luke Watson, Conor D. Rankine, Thomas J. Penfold

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  3. Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW

    Chemistry of Materials · 2022-07-13

    Dorothea Golze, Markus Hirvensalo, Patricia Hernández-León, Anja Aarva, Jarkko Etula, Toma Susi, Patrick Rinke, Tomi T. Laurila, A. Miguel

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  4. Theory+AI/ML for microscopy and spectroscopy: Challenges and opportunities

    MRS Bulletin · 2022-10-01

    Davis Unruh, Venkata Surya Chaitanya Kolluru, Arun Baskaran, Yiming Chen, Maria K. Y. Chan

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  5. Machine‐Learning Spectral Indicators of Topology

    Advanced Materials · 2022-10-04

    Nina Andrejevic, Jovana Andrejevic, Bogdan Andrei Bernevig, Nicolas Regnault, Fei Han, G. Fabbris, Thanh Nguyen, Nathan C. Drucker, Chris H. Rycroft, Mingda Li

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  6. 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

    ACS Catalysis · 2022-10-27

    Rachita Rana, Fernando D. Vila, Ambarish R. Kulkarni, Simon R. Bare

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  7. The trials and triumphs of modelling X-ray absorption spectra of transition metal phthalocyanines

    Physical Chemistry Chemical Physics · 2024-01-01

    Esma Birsen Boydas, Michael Roemelt

    Read original paper ↗ ·

  8. Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization

    Chemistry of Materials · 2024-03-01

    Yiming Chen, Chi Chen, Inhui Hwang, Michael J. Davis, Wanli Yang, Cheng‐Jun Sun, Gi‐Hyeok Lee, Dylan McReynolds, Daniel Allan, Juan Marulanda Arias, Shyue Ping Ong, Maria K. Y. Chan

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  9. Integrating Machine Learning Potential and X-ray Absorption Spectroscopy for Predicting the Chemical Speciation of Disordered Carbon Nitrides

    Chemistry of Materials · 2024-04-22

    Wonseok Jeong, Wenyu Sun, Marcos F. Calegari Andrade, Liwen F. Wan, Trevor M. Willey, Michael H. Nielsen, Tuan Anh Pham

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

    Machine Learning Science and Technology · 2024-05-24

    Thomas J. Penfold, Luke Watson, Clelia Middleton, Tudur Wyn David, Sneha Verma, Thomas J. Pope, Julia Kaczmarek, Conor D. Rankine

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  11. How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

    Microscopy Research and Technique · 2024-06-12

    Agnieszka Pręgowska, Agata Roszkiewicz, Magdalena E Osial, Michael Giersig

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  12. Machine learning approach to predict L -edge x-ray absorption spectra of light transition metal ion compounds

    Physical review. B./Physical review. B · 2025-02-04

    Johann Lüder

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  13. Atomic Metal‒Nonmetal Catalytic Pair Cooperatively Drives Efficient Enzyme‐Mimetic Catalysis

    Angewandte Chemie International Edition · 2025-07-23

    Zheye Zhang, Fuhua Li, Shibo Xi, Lewen Zheng, Xiaozhe Wang, Baojie Du, Xiao Chi, Zhongxin Chen, Hong Bin Yang, Lishuang Zhang, Dong‐Sheng Li, Bin Liu, Liping Li, Peng Chen

    Read original paper ↗ ·

  14. Atomic Metal‒Nonmetal Catalytic Pair Cooperatively Drives Efficient Enzyme‐Mimetic Catalysis

    Angewandte Chemie · 2025-07-23

    Zheye Zhang, Fuhua Li, Shibo Xi, Lewen Zheng, Xiaozhe Wang, Baojie Du, Xiao Chi, Zhongxin Chen, Hong Bin Yang, Lishuang Zhang, Dong‐Sheng Li, Bin Liu, Liping Li, Peng Chen

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  15. Hamiltonian parameter inference from resonant inelastic x-ray scattering with active learning

    Physical review. B./Physical review. B · 2025-10-08

    Márton Lájer, Xin Dai, Kipton M. Barros, Matthew R. Carbone, Steven Johnston, M. P. M. Dean

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  16. Single Cu Atom Sites on Co 3 O 4 Activate Interfacial Oxygen for Enhanced Reactivity and Selective Gas Sensing at Low Temperature

    Small · 2026-04-13

    Hamin Shin, Matteo D’Andria, Meng Yin, Jaehyun Ko, Ken Suzuki, Dong‐Ha Kim, Frank Krumeich, Andreas T. Güntner

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  17. Machine-Learning-Assisted X-ray Spectroscopy Decoding an Elemental Segregation Mechanism in Pt–Ru Binary Alloy Nanoparticles

    The Journal of Physical Chemistry Letters · 2026-04-30

    Quan Zhou, Sicong Qiao, Hongwei Shou, Heng Zhou, Shiqiang Wei, Shuangming Chen, Xiaojun Wu, 吕松 Lü Song

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  18. From Local Atomic Structure to X-ray Spectra: Absorber-Centric Machine-Learning Encoding

    The Journal of Physical Chemistry A · 2026-05-06

    Thomas J. Pope, Bowen Li, Hendrik Junkawitsch, Annika Bande, Thomas J. Penfold

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  19. Precise analysis and proper application of X-ray absorption spectroscopy

    Catal · 2026-08-20

    Hao Zhang, Yalei Fan, Jianqiu Zhu, Haisheng Yu, Jian-Qiang Wang, Zhiwei Hu, Linjuan Zhang

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  20. Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

    Advanced Intelligent Systems · 2026-08-30

    Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao

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