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

https://mdr.nims.go.jp/datasets/4b0d56e8-be2f-4781-b43b-5053a60245f6

## File

- [shrine20230330-1595-1im5f61.pdf](https://mdr.nims.go.jp/filesets/c19f81e1-45b9-4b0d-a96e-6114161bc2a6/download) ([Detail](https://mdr.nims.go.jp/filesets/c19f81e1-45b9-4b0d-a96e-6114161bc2a6.md))

## Id

4b0d56e8-be2f-4781-b43b-5053a60245f6

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-03-30T08:25:00.038606Z

## Updated at

2024-09-05T07:30:30.324169Z

## Published at

2024-09-05T07:30:30.433271Z

## Doi



## First published url

https://doi.org/10.1080/14686996.2019.1689785

## Date published

2019-12-31

## Recorded date published

2019-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Unveiling the principle descriptor for predicting the electron inelastic
    mean free path based on a machine learning framework
  title_type: original
  lang: en

## Description

- description: 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.
  description_type: abstract
  lang: eng

## Creator

- name: Xun Liu
  role: author
  organization: National Institute for Materials Science
- name: Zhufeng Hou
  role: author
- name: Dabao Lu
  role: author
- name: Bo Da
  role: author
  orcid: https://orcid.org/0000-0002-0785-8662
  organization: National Institute for Materials Science
- name: Hideki Yoshikawa
  role: author
  orcid: https://orcid.org/0000-0002-7389-8865
  organization: National Institute for Materials Science
- name: Shigeo Tanuma
  role: author
  orcid: https://orcid.org/0000-0003-2628-9941
  organization: National Institute for Materials Science
- name: Yang Sun
  role: author
- name: Zejun Ding
  role: author

## Contact agent



## Publisher

organization: Informa UK Limited

## Managing organization



## Keyword

- subject: machine learning
  schema: not_defined
- subject: inelastic mean free path
  schema: not_defined
- subject: LASSO
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Science and Technology of Advanced Materials
  issn: '14686996'
  volume: '20'
  issue: '1'
  start_page: 1090
  end_page: 1102

## Conference



## Related item



## Funding

- identifier: '11574289'
  funder_name: National Natural Science Foundation of China

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: c19f81e1-45b9-4b0d-a96e-6114161bc2a6
  filename: shrine20230330-1595-1im5f61.pdf
  content_type: application/pdf
  size: 1896597
  md5: '0836cc3032fac11529202b09185117bf'

## Thumbnail

fileset_id: c19f81e1-45b9-4b0d-a96e-6114161bc2a6
filename: shrine20230330-1595-1im5f61.pdf