# Machine learning study of universal electronic stopping cross-sections of ions in matter

https://mdr.nims.go.jp/datasets/4f5a83e5-a64c-4b16-9dde-6b949ba73502

## File

- [shrine20251222-1807645-t3s9p2.pdf](https://mdr.nims.go.jp/filesets/cc73dc40-25fb-409a-a634-d70a7a738b53/download) ([Detail](https://mdr.nims.go.jp/filesets/cc73dc40-25fb-409a-a634-d70a7a738b53.md))

## Id

4f5a83e5-a64c-4b16-9dde-6b949ba73502

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-12-22T01:36:02.102365Z

## Updated at

2025-12-22T02:12:52.069885Z

## Published at

2025-12-22T03:21:51.627114Z

## Doi



## First published url

https://doi.org/10.1016/j.net.2024.10.033

## Date published

2024-10-22

## Recorded date published

2025-3

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Machine learning study of universal electronic stopping cross-sections of
    ions in matter
  title_type: original
  lang: en

## Description

- description: Accurate electronic stopping cross-section (ESCS) database of ions
    in matter is crucial for precise simulation of radiation damage. Based on the
    experimental-cleaned database of SRIM, binary theory and unitary convolution approximation
    as well as the descriptor pool extracted from these models, we developed a universal
    machine learning ESCS database using the least absolute shrinkage and selection
    operator (LASSO) algorithm. This method allows for predictions for ion-target
    combinations with atomic numbers from 1 to 92, within the energy range from 1
    keV/u to 1 GeV/u, addressing the limitations of machine learning on training dataset.
    The database exhibits remarkable accuracy in predicting ESCS and ion depth distribution/range,
    along with robust reciprocity performance. Key descriptors are also determined,
    which closely mimic the Lindhard-Scharff-Schiott and Bohr-Bethe-Bloch formulations,
    achieved
  description_type: abstract
  lang: und

## Creator

- name: Fan Cheng
  role: author
- name: Xun Liu
  role: author
  organization: National Institute for Materials Science
- name: Qirong Zheng
  role: author
- name: Chuanguo Zhang
  role: author
- name: Bo Da
  role: author
  orcid: https://orcid.org/0000-0002-0785-8662
  organization: National Institute for Materials Science
- name: Yonggang Li
  role: author

## Contact agent



## Publisher

organization: Elsevier BV

## Managing organization



## Keyword

- subject: electronic stopping cross-sections
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by-nc-nd/4.0/
  date_licensed: 2024-10-16

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Nuclear Engineering and Technology
  issn: '17385733'
  volume: '57'
  issue: '3'
  article_number: '103271'

## Conference



## Related item



## Funding

- identifier: Y202087
  funder_name: Youth Innovation Promotion Association of the Chinese Academy of Sciences
- identifier: '12375277'
  funder_name: National Natural Science Foundation of China
- identifier: 2308085J04
  funder_name: Natural Science Foundation of Anhui Province

## 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: cc73dc40-25fb-409a-a634-d70a7a738b53
  filename: shrine20251222-1807645-t3s9p2.pdf
  content_type: application/pdf
  size: 6508216
  md5: 00ab05f11dcf893b4acf5175f3b9b00f

## Thumbnail

fileset_id: cc73dc40-25fb-409a-a634-d70a7a738b53
filename: shrine20251222-1807645-t3s9p2.pdf