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Accurate decomposition of the mixed Mn oxidation states is highly importantfor characterizing the electronic structures, charge transfer, and redoxcenters for electronic, electrocatalytic, and energy storage materials thatcontain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorptionspectroscopy (XAS) measurements of the Mn L2,3 edges are widely used for thispurpose. To date, although the measurement of the Mn L2,3 edges isstraightforward given the sample is prepared properly, an accuratedecomposition of the mix valence states of Mn remains non-trivial. For bothEELS and XAS, 2+, 3+, 4+ reference spectra need to be taken on the sameinstrument/beamline and preferably in the same experimental session because theinstrumental resolution and the energy axis offset could vary from one sessionto another. To circumvent this hurdle, in this study, we adopted a deeplearning approach and developed a calibration-free and reference-free method todecompose the oxidation state of Mn L2,3 edges for both EELS and XAS. Tosynthesize physics-informed and ground-truth labeled training datasets, wecreated a forward model that takes into account plural scattering,instrumentation broadening, noise, and energy axis offset. With that, wecreated a 1.2 million-spectrum database with a three-element oxidation statecomposition label. The library includes a sufficient variety of data includingboth EELS and XAS spectra. By training on this large database, ourconvolutional neural network achieves 85% accuracy on the validation dataset.We tested the model and found it is robust against noise (down to PSNR of 10)and plural scattering (up to t/{\lambda} = 1). We further validated the modelagainst spectral data that were not used in training.

MnEdgeNet -- Accurate Decomposition of Mixed Oxidation States for Mn XAS and EELS L2,3 Edges without Reference and Calibration