Prediction of the Cu oxidation state from EELS and XAS spectra using supervised machine learning  

作　　者：Samuel P.Gleason Deyu Lu Jim Ciston 

机构地区：[1]National Center for Electron Microscopy Facility,Molecular Foundry,Lawrence Berkeley National Laboratory,Berkeley,CA,USA [2]Department of Chemistry,University of California,Berkeley,CA,USA [3]Center for Functional Nanomaterials,Brookhaven National Laboratory,Upton,NY,USA

出　　处：《npj Computational Materials》2024年第1期927-936,共10页计算材料学(英文)

摘　　要：Electron energy loss spectroscopy(EELS)and X-ray absorption spectroscopy(XAS)provide detailed information about bonding,distributions and locations of atoms,and their coordination numbers and oxidation states.However,analysis of XAS/EELS data often relies on matching an unknown experimental sample to a series of simulated or experimental standard samples.This limits analysis throughput and the ability to extract quantitative information from a sample.In this work,we have trained a random forest model capable of predicting the oxidation state of copper based on its L-edge spectrum.Our model attains an R2 score of 0.85 and a root mean square error of 0.24 on simulated data.It has also successfully predicted experimental L-edge EELS spectra taken in thiswork and XAS spectra extracted from the literature.We further demonstrate the utility of this model by predicting simulated and experimental spectra ofmixed valence samples generated by this work.This model can be integrated into a real-time EELS/XAS analysis pipeline on mixtures of copper-containing materials of unknown composition and oxidation state.By expanding the training data,this methodology can be extended to data-driven spectral analysis of a broad range of materials.

关 键 词：EELS SPECTRA analysis 

分 类 号：TG1[金属学及工艺—金属学]