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

Xun Liu; Lihao Yang; Zhufeng Hou; Bo Da; Kenji Nagata; Hideki Yoshikawa; Shigeo Tanuma; Yang Sun; Zejun Ding

doi:10.1103/PhysRevMaterials.5.033802

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

Xun Liu (National Institute for Materials Science) ; Lihao Yang ; Zhufeng Hou ; Bo Da (National Institute for Materials Science) ; Kenji Nagata (National Institute for Materials Science) ; Hideki Yoshikawa (National Institute for Materials Science) ; Shigeo Tanuma (National Institute for Materials Science) ; Yang Sun ; Zejun Ding

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Xun Liu, Lihao Yang, Zhufeng Hou, Bo Da, Kenji Nagata, Hideki Yoshikawa, Shigeo Tanuma, Yang Sun, Zejun Ding. Machine learning approach for the prediction of electron inelastic mean free paths. Physical Review Materials. 2021, 5 (3), 33802-33802. https://doi.org/10.1103/PhysRevMaterials.5.033802

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(abstract)

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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