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Japan (Grant No. JPMJMI22H1). The grantors had no role in the experiment design, collection, analysis
and interpretation of data, and writing of the manuscript.
Conflicts of interest
Manzhos, S. is a member of the Editorial Board of Journal of Materials Informatics, and Liu, Y. is a member
of the Youth Editorial Board of the same journal. Liu, Y. also served as a Guest Editor for the special issue
titled “Unlocking the AI Future of Materials Science”: Selected Papers from the International Workshop on
Data-driven Computational and Theoretical Materials Design (DCTMD). They were not involved in any
steps of the editorial processing, notably including reviewer selection, manuscript handling, or decision-
making. The other authors declare no conflicts of interest.
Ethical approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Copyright
© The Author(s) 2025.
REFERENCES
1. Xie, T.; Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material
properties. Phys. Rev. Lett. 2018, 120, 145301. DOI PubMed
2. Ward, L.; Liu, R.; Krishna, A.; et al. Including crystal structure attributes in machine learning models of formation energies via
Voronoi tessellations. Phys. Rev. B. 2017, 96, 024104. DOI
3. Wang, T.; Tan, X.; Wei, Y.; Jin, H. Accurate bandgap predictions of solids assisted by machine learning. Mater. Today. Commun.
2021, 29, 102932. DOI
4. Alsalman, M.; Alqahtani, S. M.; Alharbi, F. H. Bandgap energy prediction of senary zincblende III–V semiconductor compounds
using machine learning. Mater. Sci. Semicond. Process. 2023, 161, 107461. DOI
5. Li, Y.; Wu, Y.; Han, Y.; et al. Local environment interaction-based machine learning framework for predicting molecular adsorption
energy. J. Mater. Inf. 2024, 4, 4. DOI
6. Kohn, W.; Sham, L. J. Self-consistent equations including exchange and correlation effects. Phys. Rev. 1965, 140, A1133-8. DOI
4+
7. Ming, H.; Zhou, Y.; Molokeev, M. S.; et al. Machine-learning-driven discovery of Mn -doped red-emitting fluorides with short
excited-state lifetime and high efficiency for mini light-emitting diode displays. ACS. Mater. Lett. 2024, 6, 1790-800. DOI
8. Bone, J. M.; Childs, C. M.; Menon, A.; et al. Hierarchical machine learning for high-fidelity 3D printed biopolymers. ACS. Biomater.
Sci. Eng. 2020, 6, 7021-31. DOI
9. Zhu, J.; Ding, L.; Sun, G.; Wang, L. Accelerating design of glass substrates by machine learning using small-to-medium datasets.
Ceram. Int. 2024, 50, 3018-25. DOI
10. Shim, E.; Tewari, A.; Cernak, T.; Zimmerman, P. M. Machine learning strategies for reaction development: toward the low-data limit.
J. Chem. Inf. Model. 2023, 63, 3659-68. DOI PubMed PMC
11. Im, J.; Lee, S.; Ko, T.; Kim, H. W.; Hyon, Y.; Chang, H. Identifying Pb-free perovskites for solar cells by machine learning. npj.
Comput. Mater. 2019, 5, 177. DOI
12. Yang, J.; Manganaris, P.; Mannodi-Kanakkithodi, A. Discovering novel halide perovskite alloys using multi-fidelity machine learning
and genetic algorithm. J. Chem. Phys. 2024, 160, 064114. DOI PubMed
13. Liu, C.; Fujita, E.; Katsura, Y.; et al. Machine learning to predict quasicrystals from chemical compositions. Adv. Mater. 2021, 33,
2102507. DOI
14. Isayev, O.; Oses, C.; Toher, C.; Gossett, E.; Curtarolo, S.; Tropsha, A. Universal fragment descriptors for predicting properties of
inorganic crystals. Nat. Commun. 2017, 8, 15679. DOI PubMed PMC
15. Liu, Y.; Wang, J.; Xiao, B.; Shu, J. Accelerated development of hard high-entropy alloys with data-driven high-throughput
experiments. J. Mater. Inf. 2022, 2, 3. DOI
16. Christensen, A. S.; Bratholm, L. A.; Faber, F. A.; Anatole von Lilienfeld, O. FCHL revisited: faster and more accurate quantum
machine learning. J. Chem. Phys. 2020, 152, 044107. DOI PubMed
17. Bartók, A. P.; Kondor, R.; Csányi, G. On representing chemical environments. Phys. Rev. B. 2013, 87, 184115. DOI
18. Rogers, D.; Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model. 2010, 50, 742-54. DOI PubMed

