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AI and AI-assisted tools statement
During the preparation of this manuscript, the AI tool DeepSeek (version V3.2, released 2025-09-06) was
used solely for language editing. The tool did not influence the study design, data collection, analysis,
interpretation, or the scientific content of the work. All authors take full responsibility for the accuracy,
integrity, and final content of the manuscript.

Financial support and sponsorship
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62102431 and
U20A20231).

Conflicts of interest
All authors declared 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.

Supplementary Materials
Supplementary Materials

REFERENCES
1. Gormley, A. J.; bookb, M. A. Machine learning in combinatorial polymer chemistry. Nat. Rev. Mater. 2021, 6, 642-4. DOI PubMed
PMC
2. Wang, S.; Yue, H.; Yuan, X. Accelerating polymer discovery with uncertainty-guided PGCNN: explainable AI for predicting properties
and mechanistic insights. J. Chem. Inf. Model. 2024, 64, 5500-9. DOI PubMed
3. Carvalho, R. P.; Marchiori, C. F. N.; Brandell, D.; Araujo, C. M. Artificial intelligence driven in-silico discovery of novel organic
lithium-ion battery cathodes. Energy. Storage. Mater. 2022, 44, 313-25. DOI
4. Bhowmik, A.; Berecibar, M.; Casas‐Cabanas, M.; et al. Implications of the BATTERY 2030+ AI‐assisted toolkit on future low‐TRL
battery discoveries and chemistries. Adv. Energy. Mater. 2021, 12, 2102698. DOI
5. Zhou, Z.; Shang, Y.; Liu, X.; Yang, Y. A generative deep learning framework for inverse design of compositionally complex bulk
metallic glasses. npj. Comput. Mater. 2023, 9, 15. DOI
6. Basu, B.; Gowtham, N. H.; Xiao, Y.; Kalidindi, S. R.; Leong, K. W. Biomaterialomics: data science-driven pathways to develop
fourth-generation biomaterials. Acta. Biomater. 2022, 143, 1-25. DOI PubMed
7. Singh, A. V.; Rosenkranz, D.; Ansari, M. H. D.; et al. Artificial intelligence and machine learning empower advanced biomedical
material design to toxicity prediction. Adv. Intell. Syst. 2020, 2, 2000084. DOI
8. Debnath, A.; Krajewski, A. M.; Sun, H.; et al. Generative deep learning as a tool for inverse design of high entropy refractory alloys. J.
Mater. Inf. 2021, 1, 3. DOI
9. Hart, G. L. W.; Mueller, T.; Toher, C.; Curtarolo, S. Machine learning for alloys. Nat. Rev. Mater. 2021, 6, 730-55. DOI
10. Kononova, O.; He, T.; Huo, H.; Trewartha, A.; Olivetti, E. A.; Ceder, G. Opportunities and challenges of text mining in aterials research.
iScience 2021, 24, 102155. DOI PubMed PMC
11. Sierepeklis, O.; Cole, J. M. A thermoelectric materials database auto-generated from the scientific literature using ChemDataExtractor.
Sci. Data. 2022, 9, 648. DOI PubMed PMC
12. Kumar, P.; Kabra, S.; Cole, J. M. Auto-generating databases of Yield Strength and Grain Size using ChemDataExtractor. Sci. Data.
2022, 9, 292. DOI
13. Wang, W.; Jiang, X.; Tian, S.; et al. Automated pipeline for superalloy data by text mining. npj. Comput. Mater. 2022, 8, 9. DOI
14. Swain, M. C.; Cole, J. M. ChemDataExtractor: a toolkit for automated extraction of chemical information from the scientific literature. J.
Chem. Inf. Model. 2016, 56, 1894-904. DOI PubMed
15. Widiastuti, N. I. Convolution neural network for text mining and natural language processing. IOP. Conf. Ser. Mater. Sci. Eng. 2019,
662, 052010. DOI