Page 76 - Read Online
P. 76

Tang et al. J. Mater. Inf. 2025, 5, 38  https://dx.doi.org/10.20517/jmi.2025.05  Page 17 of 19

               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
   71   72   73   74   75   76   77   78   79   80   81