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Page 26 of 29 Liu et al. J. Mater. Inf. 2026, 6, 18

CONCLUSIONS
This study develops an interpretable multi-objective design framework for optimizing high-performance
α-titanium alloys by integrating domain knowledge with data-driven ML approaches. By extracting critical
physical descriptors - such as Fermi energy and strain rate - to construct high-precision RF models, and
subsequently integrating these models into an NSGA-II optimization framework, over 200,000 candidate
compositions were efficiently screened to identify a Pareto-optimal set of near-α Ti-Al-V-Mo-Zr-Sn alloys.
These optimized compositions demonstrate superior dynamic strength and reduced adiabatic temperature
rise compared to the benchmark TA15 alloy. Overall, this work establishes an effective and interpretable
paradigm for accelerating the discovery of advanced alloys, offering a strategic pathway to address the
strength-ductility trade-off under extreme conditions.

DECLARATIONS
Acknowledgments
This research was supported by the National Key Research and Development Program Project (No.
2024YFE0213600). The authors gratefully acknowledge this funding, which was instrumental in the study’s
design, data collection, and analysis.

Authors’ contributions
Conceptualization, methodology, data curation, investigation, formal analysis, writing - original draft
preparation: Liu, S.
Writing - original draft preparation, supervision, methodology, editing, validation, project administration,
funding acquisition: Wang, W. Y.; Li, P.; Song, H.; Gao, X.
Data curation, investigation, formal analysis, writing - original draft preparation: Fan, X.; Ye, H.
Conceptualization, methodology, editing, project administration: Ibragim, M.
Conceptualization, supervision, methodology, editing, validation, project administration:
Yar-Mukhamedova, G.; Zellele, D.
Supervision, conceptualization, methodology, editing, project administration, funding acquisition: Li, J.;
Wang, W. Y.
All authors have read and agreed to the published version of the manuscript.

Availability of data and materials
The original contributions presented in this study are included in the article. Further inquiries can be
directed to the corresponding authors.

AI and AI-assisted tools statement
Not applicable.

Financial support and sponsorship
This research was supported by the National Key Research and Development Program Project (No.
2024YFE0213600).

Conflicts of interest
Wang, W. Y. is an Editor on the Junior Editorial Board of the Journal of Materials Informatics. Wang, W. Y.
was not involved in any steps of editorial processing, notably including the selection of reviewers, manuscript
handling, and decision-making. The other authors declare that there are no conflicts of interest.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
© The Author(s) 2026.