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¹National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
²Department of Chemistry, University of California, Berkeley, California, USA
³Computing and Data Sciences Directorate, Brookhaven National Laboratory, Upton, New York 11973, USA
⁴Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, USA

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Articles Citing

CuXASNet: Rapid and accurate prediction of copper $L$ -edge x-ray absorption spectra using machine learning

Advancing AI-Driven Analysis in X-ray Absorption Spectroscopy: Spectral Domain Mapping and Universal Models

DeepFit: Physically and Chemically Informed XAS-Structure Fitting Made Simple

Deciphering the Solvation Structure of Aqueous ZnCl2 Solutions from X-ray Absorption Spectra Using the Interpretable Graph Neural Network

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

Universal Rapid Machine Learning Models for Predicting Unconvoluted and Convoluted X-ray Absorption Spectra

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Articles Citing

CuXASNet: Rapid and accurate prediction of copper L-edge x-ray absorption spectra using machine learning

Advancing AI-Driven Analysis in X-ray Absorption Spectroscopy: Spectral Domain Mapping and Universal Models

DeepFit: Physically and Chemically Informed XAS-Structure Fitting Made Simple

Deciphering the Solvation Structure of Aqueous ZnCl2 Solutions from X-ray Absorption Spectra Using the Interpretable Graph Neural Network

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

Universal Rapid Machine Learning Models for Predicting Unconvoluted and Convoluted X-ray Absorption Spectra

CuXASNet: Rapid and accurate prediction of copper $L$ -edge x-ray absorption spectra using machine learning