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

https://mdr.nims.go.jp/datasets/e586cd2d-4724-40d0-bc8a-7664a4cb1d10

## File

- [manuscript.docx](https://mdr.nims.go.jp/filesets/1279dfe5-3478-4148-aea1-935c1ff7c772/download) ([Detail](https://mdr.nims.go.jp/filesets/1279dfe5-3478-4148-aea1-935c1ff7c772.md))

## Id

e586cd2d-4724-40d0-bc8a-7664a4cb1d10

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-03-30T08:18:06.618221Z

## Updated at

2024-01-05T13:13:49.318261Z

## Published at

2023-04-11T01:40:43.244914Z

## Doi

https://doi.org/10.48505/nims.3953

## First published url

https://doi.org/10.1103/PhysRevMaterials.5.033802

## Date published

2021-03-24

## Recorded date published

2021-3

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: Machine learning approach for the prediction of electron inelastic mean free
    paths
  title_type: original
  lang: en

## Description

- description: 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.
  description_type: abstract
  lang: eng

## Creator

- name: Xun Liu
  role: author
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Lihao Yang
  role: author
- name: Zhufeng Hou
  role: author
- name: Bo Da
  role: author
  orcid: https://orcid.org/0000-0002-0785-8662
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Kenji Nagata
  role: author
  orcid: https://orcid.org/0000-0001-9894-4461
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Hideki Yoshikawa
  role: author
  orcid: https://orcid.org/0000-0002-7389-8865
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Shigeo Tanuma
  role: author
  orcid: https://orcid.org/0000-0003-2628-9941
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Yang Sun
  role: author
- name: Zejun Ding
  role: author

## Contact agent



## Publisher

organization: American Physical Society (APS)

## Managing organization



## Keyword

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

## Rights

- identifier: http://rightsstatements.org/vocab/InC/1.0/

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## Embargo



## Journal

- title: Physical Review Materials
  issn: '24759953'
  volume: '5'
  issue: '3'
  start_page: 33802
  end_page: 33802

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## Fileset

- id: 1279dfe5-3478-4148-aea1-935c1ff7c772
  filename: manuscript.docx
  content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document
  size: 1294442
  md5: 8bfcedda54015c5132753311dbd58ffc

## Thumbnail

fileset_id: 1279dfe5-3478-4148-aea1-935c1ff7c772
filename: manuscript.docx