An interpretable learning framework for exploring superelastic degradation of NiTi shape memory alloys using multimodal data.

Yuejie Hu, Haiyang Wang, Ming Chen, Gang Chen, Chuanjie Wang, Bin Guo, M W Fu

Abstract read

In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

An interpretable learning framework for exploring superelastic degradation of NiTi shape memory alloys using multimodal data.Nature communications · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yuejie HuSchool of Materials Science and Engineering, Harbin Institute of Technology at Weihai, Weihai, China.ORCID http://orcid.org/0000-0001-9026-7902

Haiyang WangSchool of Materials Science and Engineering, Harbin Institute of Technology at Weihai, Weihai, China.

Ming ChenState Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, China.

Gang ChenSchool of Materials Science and Engineering, Harbin Institute of Technology at Weihai, Weihai, China.

Chuanjie WangSchool of Materials Science and Engineering, Harbin Institute of Technology at Weihai, Weihai, China. cjwang@hitwh.edu.cn.ORCID http://orcid.org/0000-0002-5171-7322

Bin GuoKey Laboratory of Micro-Systems and Micro-Structures Manufacturing (Harbin Institute of Technology), Ministry of Education, Harbin, China.

M W FuDepartment of Mechanical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China. mmmwfu@polyu.edu.hk.ORCID http://orcid.org/0000-0003-4173-7159

Funding

Natural Science Foundation of Shandong Province (Shandong Provincial Natural Science Foundation) No. ZR2025MS803

6 · The paper itself

Abstract

Superelastic degradation (SED), a progressive loss of functionality in NiTi shape memory alloys under cyclic loading, remains challenging to characterize precisely, thereby constraining their engineering applications and broader adoption. In this study, an interpretable learning framework was proposed to predict the SED of NiTi alloys and uncover the degradation mechanisms using interpretability analysis methods. The framework incorporates multi-source microstructure and loading conditions through a multi-branch architecture that effectively decouples and integrates heterogeneous features, achieving an R

Identifiers

PMID42811070

DOI10.1038/s41467-026-77250-4

PMCPMC13623974

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.