Machine learning approach for the prediction of electron inelastic mean free paths

MDR Open Deposited

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.

First published at
Resource type
Date published
  • 24/03/2021
Rights statement