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

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. The resulting framework not only provides higher average accuracy and stability but also reveals the physics meanings of several newly found descriptors. Using the identified principle descriptors, a complete physics picture of electron IMFPs is obtained,

SUBMITTER: Liu X 

PROVIDER: S-EPMC6882444 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature