# Machine learning prediction of Young's modulus in multi component titanium based biomedical alloys using extended thermodynamic descriptors

https://mdr.nims.go.jp/datasets/adf74d13-9f90-4b57-829a-27294d186789

## File

- [TSTM-2025-0079_data.zip](https://mdr.nims.go.jp/filesets/a47bb3f8-24ea-4578-a82c-aa6d9e947af4/download) ([Detail](https://mdr.nims.go.jp/filesets/a47bb3f8-24ea-4578-a82c-aa6d9e947af4.md))
- [Manuscript_clean.pdf](https://mdr.nims.go.jp/filesets/75bde451-1472-41db-9741-6ea6bbdf893d/download) ([Detail](https://mdr.nims.go.jp/filesets/75bde451-1472-41db-9741-6ea6bbdf893d.md))
- [Supplementary information.pdf](https://mdr.nims.go.jp/filesets/bb67fb54-961c-4b5a-b6f2-73eb549072d9/download) ([Detail](https://mdr.nims.go.jp/filesets/bb67fb54-961c-4b5a-b6f2-73eb549072d9.md))

## Id

adf74d13-9f90-4b57-829a-27294d186789

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-07-08T06:20:38.479958Z

## Updated at

2026-07-08T07:58:38.755491Z

## Published at

2026-07-08T09:24:55.852800Z

## Doi

https://doi.org/10.48505/nims.6388

## First published url

https://doi.org/10.1080/27660400.2026.2691685

## Date published

2026-12-31

## Recorded date published

2026-12-31

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: Machine learning prediction of Young's modulus in multi component titanium
    based biomedical alloys using extended thermodynamic descriptors
  title_type: original
  lang: en

## Description

- description: The development of low-modulus titanium alloys for biomedical implants
    is frequently constrained by the resource-intensive nature of experimental discovery
    and the limited size of available datasets. To address this, this study presents
    a machine‑learning framework trained on a comprehensive dataset of 689 alloy compositions,
    extending beyond conventional systems to include multicomponent alloys described
    using thermodynamic descriptors borrowed from the high-entropy alloy literature.
    By integrating physicochemical and thermodynamic descriptors, an optimized XGBoost
    model was developed to predict Young’s modulus. The model achieved a test R2 of
    0.69 and a test MAE of 9.67 GPa. However, predictive accuracy is comparatively
    lower in the low-modulus regime (below 50 GPa), which set it for the primary target
    range for implant applications. External validation against 24 independent alloys
    was performed to assess model performance across diverse chemical spaces. Feature
    importance analysis revealed that configurational mixing entropy and molybdenum
    equivalence are critical determinants of stiffness and phase stability. These
    results demonstrate that integrating thermodynamic descriptors into composition-based
    models improves predictive capability and may support preliminary, coarse-grained
    screening of candidate low-modulus biomedical alloys prior to experimental synthesis.
  description_type: abstract
  lang: en

## Creator

- name: Hassan Ahmad
  role: author
  organization: Pakistan Institute of Engineering and Applied Sciences (PIEAS)
  department: Department of Metallurgy and Materials Engineering
- name: Muhammad Haider
  role: author
- name: Zafar Iqbal
  role: author
- name: Muhammad Zarif
  role: author
- name: Syed Mujtaba Ul Hassan
  role: author

## Contact agent



## Publisher

organization: Taylor & Francis

## Managing organization



## Keyword

- subject: Titanium alloys
  schema: not_defined
- subject: machine learning
  schema: not_defined
- subject: biomedical alloys
  schema: not_defined
- subject: Young’s modulus prediction
  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: '27660400'
  volume: '6'
  article_number: '2691685'

## Conference



## Related item



## Funding



## 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: a47bb3f8-24ea-4578-a82c-aa6d9e947af4
  filename: TSTM-2025-0079_data.zip
  content_type: application/zip
  size: 23109
  md5: ef3fe577f4dbecd5784b346b9f80c55f
- id: 75bde451-1472-41db-9741-6ea6bbdf893d
  filename: Manuscript_clean.pdf
  content_type: application/pdf
  size: 978892
  md5: f44de620440bd7dd7bd54eafd4d60a6a
- id: bb67fb54-961c-4b5a-b6f2-73eb549072d9
  filename: Supplementary information.pdf
  content_type: application/pdf
  size: 292560
  md5: 694799d77c0c6938521b5b1b9d528cc8

## Thumbnail

fileset_id: bb67fb54-961c-4b5a-b6f2-73eb549072d9
filename: Supplementary information.pdf