Page 62 - Read Online
P. 62

Zhu et al. J. Mater. Inf. 2025, 5, 8  https://dx.doi.org/10.20517/jmi.2024.76   Page 19 of 20

               REFERENCES
               1.       Russell, A. Ductility in intermetallic compounds. Adv. Eng. Mater. 2003, 5, 629-39.  DOI
               2.       Taub, A. I.; Fleischer, R. L. Intermetallic compounds for high-temperature structural use. Science 1989, 243, 616-21.  DOI  PubMed
               3.       Dshemuchadse, J.; Steurer, W. Some statistics on intermetallic compounds. Inorg. Chem. 2015, 54, 1120-8.  DOI  PubMed
               4.       Yamaguchi, M.; Inui, H.; Ito, K. High-temperature structural intermetallics. Acta. Mater. 2000, 48, 307-22.  DOI
               5.       Kimura, Y.; Pope, D. P. Ductility and toughness in intermetallics. Intermetallics 1998, 6, 567-71.  DOI
               6.       Zhu, D.; Pan, K.; Wu, H.; et al. Identifying intrinsic factors for ductile-to-brittle transition temperatures in Fe–Al intermetallics via
                   machine learning. J. Mater. Res. Technol. 2023, 26, 8836-45.  DOI
               7.       Ravindran, P.; Asokamani, R. Correlation between electronic structure, mechanical properties and phase stability in intermetallic
                   compounds. Bull. Mater. Sci. 1997, 20, 613-22.  DOI
               8.       Kim, S. H.; Kim, H.; Kim, N. J. Brittle intermetallic compound makes ultrastrong low-density steel with large ductility. Nature 2015,
                   518, 77-9.  DOI  PubMed
               9.       Crawley, A. F. Densities of liquid metals and alloys. Int. Metall. Rev. 1974, 19, 32-48.  DOI
               10.      Fleischer, R. L. High-strength, high-temperature intermetallic compounds. J. Mater. Sci. 1987, 22, 2281-8.  DOI
               11.      Stoloff, N.; Liu, C.; Deevi, S. Emerging applications of intermetallics. Intermetallics 2000, 8, 1313-20.  DOI
               12.      Uenishi, K.; Kobayashi, K. Processing of intermetallic compounds for structural applications at high temperature. Intermetallics 1996,
                   4, S95-101.  DOI
               13.      Paul, A. R.; Mukherjee, M.; Singh, D. A critical review on the properties of intermetallic compounds and their application in the
                   modern manufacturing. Cryst. Res. Technol. 2022, 57, 2100159.  DOI
               14.      Fatima, B.; Chouhan, S. S.; Acharya, N.; Sanyal, S. P. Density functional study of XRh (X=Sc, Y, Ti and Zr) intermetallic compounds.
                   Comput. Mater. Sci. 2014, 89, 205-15.  DOI
               15.      Lu, Z. W.; Wei, S.; Zunger, A.; Frota-Pessoa, S.; Ferreira, L. G. First-principles statistical mechanics of structural stability of
                   intermetallic compounds. Phys. Rev. B. Condens. Matter. 1991, 44, 512-44.  DOI  PubMed
               16.      Zhu, D.; Pan, K.; Wu, Y.; et al. Improved material descriptors for bulk modulus in intermetallic compounds via machine learning.
                   Rare. Met. 2023, 42, 2396-405.  DOI
               17.      Oliynyk, A. O.; Mar, A. Discovery of intermetallic compounds from traditional to machine-learning approaches. Acc. Chem. Res.
                   2018, 51, 59-68.  DOI  PubMed
               18.      Medasani, B.; Gamst, A.; Ding, H.; et al. Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine
                   learning. npj. Comput. Mater. 2016, 2, 1.  DOI
               19.      Nie, M.; Chen, D.; Wang, D. Reinforcement learning on graphs: a survey. IEEE. Trans. Emerg. Top. Comput. Intell. 2023, 7, 1065-82.
                   DOI
               20.      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
               21.      Park, C. W.; Wolverton, C. Developing an improved crystal graph convolutional neural network framework for accelerated materials
                   discovery. Phys. Rev. Mater. 2020, 4, 063801.  DOI
               22.      Reiser, P.; Neubert, M.; Eberhard, A.; et al. Graph neural networks for materials science and chemistry. Commun. Mater. 2022, 3, 93.
                   DOI  PubMed  PMC
               23.      Dreger, M.; Eslamibidgoli, M. J.; Eikerling, M. H.; Malek, K. Synergizing ontologies and graph databases for highly flexible
                   materials-to-device workflow representations. J. Mater. Inf. 2023, 3, 2.  DOI
               24.      Choudhary, K.; Decost, B. Atomistic line graph neural network for improved materials property predictions. npj. Comput. Mater.
                   2021, 7, 650.  DOI
               25.      Wu, X.; Wang, H.; Gong, Y.; et al. Graph neural networks for molecular and materials representation. J. Mater. Inf. 2023, 3, 12.  DOI
               26.      Fung, V.; Zhang, J.; Juarez, E.; Sumpter, B. G. Benchmarking graph neural networks for materials chemistry. npj. Comput. Mater.
                   2021, 7, 554.  DOI
               27.      Dai, M.; Demirel, M. F.; Liang, Y.; Hu, J. Graph neural networks for an accurate and interpretable prediction of the properties of
                   polycrystalline materials. npj. Comput. Mater. 2021, 7, 574.  DOI
               28.      Jain, A.; Ong, S. P.; Hautier, G.; et al. Commentary: the materials project: a materials genome approach to accelerating materials
                   innovation. APL. Materials. 2013, 1, 011002.  DOI
               29.      Chicco, D.; Warrens, M. J.; Jurman, G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE,
                   MSE and RMSE in regression analysis evaluation. PeerJ. Comput. Sci. 2021, 7, e623.  DOI  PubMed  PMC
               30.      Chai, T.; Draxler, R. R. Root mean square error (RMSE) or mean absolute error (MAE)? - Arguments against avoiding RMSE in the
                   literature. Geosci. Model. Dev. 2014, 7, 1247-50.  DOI
               31.      Zhu, D.; Wu, H.; Hou, F.; et al. A transfer learning strategy for tensile strength prediction in austenitic stainless steel across
                   temperatures. Scr. Mater. 2024, 251, 116210.  DOI
               32.      Bradley, A. P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern. Recognit. 1997, 30,
                   1145-59.  DOI
               33.      Yoshida Laboratory. XenonPy. 2020. https://github.com/yoshida-lab/XenonPy. (accessed 2025-02-05).
               34.      Fessler, J.; Sutton, B. Nonuniform fast fourier transforms using min-max interpolation. IEEE. Trans. Signal. Process. 2003, 51, 560-
   57   58   59   60   61   62   63   64   65   66   67