Machine learning approach for the prediction of electron inelastic mean free paths
The prediction of electron inelastic mean free paths (IMFPs) from simple material parameters is a challenging problem in studies using electron spectroscopy and microscopy. Herein, we propose a machine learning approach to predict IMFPs from some basic material property data. The machine learning model showed excellent performance based on the calculated IMFPs for a group of 41 elemental materials (Li, Be, C (graphite), C (diamond), C (glassy), Na, Mg, Al, Si, K, Sc, Ti, V, Cr, Fe, Co, Ni, Cu, Ge, Y, Nb, Mo, Ru, Rh, Pd, Ag, In, Sn, Cs, Gd, Tb, Dy, Hf, Ta, W, Re, Os, Ir, Pt, Au, Bi) from our previous work by Shinotsuka et al., which was comparable to that of the robust TPP-2M formula (by Tanuma, Powell and Penn). The developed machine learning model was then extended to materials that do not have reported IMFPs in the Shinotsuka et al. database.
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