# Data-driven analysis of hydrogen embrittlement in martensitic steels with interpretable machine learning

https://mdr.nims.go.jp/datasets/c2f1bfe5-1526-41cc-a54f-0b797e495999

## File

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## Id

c2f1bfe5-1526-41cc-a54f-0b797e495999

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-06-16T02:27:00.497566Z

## Updated at

2026-06-17T00:57:29.580019Z

## Published at

2026-06-17T03:40:02.004817Z

## Doi



## First published url

https://doi.org/10.1016/j.matdes.2026.116083

## Date published

2026-04-23

## Recorded date published

2026-6

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Data-driven analysis of hydrogen embrittlement in martensitic steels with
    interpretable machine learning
  title_type: original
  lang: en

## Description

- description: A data-driven analytical framework integrating machine learning (ML),
    SHAP (SHapley Additive exPlanations) analysis, and symbolic regression (SR) was
    developed and applied to quantitatively analyze and interpret hydrogen embrittlement
    behavior in martensitic steels. Slow strain rate tests (SSRT) were conducted on
    JIS-SCM440 steels with systematically varied diffusible hydrogen contents, phosphorus
    contents, and tempering conditions to construct an experimental database. The
    ML model accurately predicted the notch tensile strength, and SHAP analysis quantitatively
    evaluated the contributions and interactions of the key factors. The SHAP results
    were then simplified to extract dominant trends, which were interpreted from a
    metallurgical perspective to elucidate the characteristic dependencies of hydrogen
    embrittlement on hydrogen content, phosphorus content, and tempering conditions.
    Based on these insights, SR was applied to formulate explicit and interpretable
    equations representing these relationships. The resulting model not only reproduces
    the characteristic physical behavior associated with hydrogen embrittlement but
    also establishes a quantitative framework linking data-driven analysis and metallurgical
    understanding, providing a rational basis for the design and safety assessment
    of high-strength martensitic steels used in hydrogen infrastructure applications.
  description_type: abstract
  lang: und

## Creator

- name: Houichi Kitano
  role: author
  orcid: https://orcid.org/0000-0002-0778-574X
- name: Yuuji Kimura
  role: author
  orcid: https://orcid.org/0000-0002-8907-0704
- name: Akinobu Shibata
  role: author
  orcid: https://orcid.org/0000-0001-8577-6411

## Contact agent



## Publisher

organization: Elsevier BV

## Managing organization



## Keyword

- subject: Hydrogen embrittlement
  schema: not_defined
- subject: Martensitic steel
  schema: not_defined
- subject: Machine learning
  schema: not_defined
- subject: hap (shapley additive explanations)
  schema: not_defined
- subject: Symbolic regression
  schema: not_defined

## Rights

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

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## Data origin



## Embargo



## Journal

- title: Materials & Design
  issn: '02641275'
  volume: '266'
  article_number: '116083'

## Conference



## Related item



## Funding

- funder_name: Government of Japan Ministry of Education Culture Sports Science and
    Technology

## Instrument



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## Specimen



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## Fileset

- id: 446bcf3a-4e10-4740-a020-fe6259914ac5
  filename: main.pdf
  content_type: application/pdf
  size: 15745468
  md5: ffdd4633076fc47bc3904c24b07641a0

## Thumbnail

fileset_id: 446bcf3a-4e10-4740-a020-fe6259914ac5
filename: main.pdf