Unveiling the principle descriptor for predicting the electron inelastic mean free path based on a machine learning framework

Xun Liu; Zhufeng Hou; Dabao Lu; Bo Da; Hideki Yoshikawa; Shigeo Tanuma; Yang Sun; Zejun Ding

doi:10.1080/14686996.2019.1689785

論文 Unveiling the principle descriptor for predicting the electron inelastic mean free path based on a machine learning framework

Xun Liu (National Institute for Materials Science) ; Zhufeng Hou ; Dabao Lu ; Bo Da (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, Zhufeng Hou, Dabao Lu, Bo Da, Hideki Yoshikawa, Shigeo Tanuma, Yang Sun, Zejun Ding. Unveiling the principle descriptor for predicting the electron inelastic mean free path based on a machine learning framework. Science and Technology of Advanced Materials. 2019, 20 (1), 1090-1102. https://doi.org/10.1080/14686996.2019.1689785

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(abstract)

The TPP-2M formula is the most popular empirical formula for the estimation of the electron inelastic mean free paths (IMFPs) in solids from several simple material parameters. The TPP-2M formula, however, poorly describes several materials because it relies heavily on the traditional least-squares analysis. Herein, we propose a new framework based on machine learning to overcome the weakness. This framework allows a selection from an enormous number of combined terms (descriptors) to build a new formula that describes the electron IMFPs.

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