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               2.  Rao, Z.; Tung, P. Y.; Xie, R.; et al. Machine learning-enabled high-entropy alloy discovery. Science 2022, 378, 78-85. DOI PubMed
               3.  Sohail, Y.; Zhang, C.; Xue, D.; et al. Machine-learning design of ductile FeNiCoAlTa alloys with high strength. Nature 2025, 643,
                  119-24. DOI PubMed PMC
               4.  Wang, T.; Pan, R.; Martins, M. L.; et al. Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active
                  materials for aqueous supercapacitors. Nat. Commun. 2023, 14, 4607. DOI PubMed PMC
               5.  Wu, J.; Torresi, L.; Hu, M.; et al. Inverse design workflow discovers hole-transport materials tailored for perovskite solar cells. Science.
                  2024, 386, 1256-64. DOI PubMed
               6.  Tian, Y.; Hu, B.; Dang, P.; Pang, J.; Zhou, Y.; Xue, D. Noise-aware active learning to develop high-temperature shape memory alloys
                  with large latent heat. Adv. Sci. 2024, 11, e2406216. DOI PubMed PMC
               7.  Xian, Y.; Dang, P.; Tian, Y.; et al. Compositional design of multicomponent alloys using reinforcement learning. Acta. Mater. 2024,
                  274, 120017. DOI
               8.  Wei, Q.; Cao, B.; Yuan, H.; et al. Divide and conquer: machine learning accelerated design of lead-free solder alloys with high strength
                  and high ductility. npj. Comput. Mater. 2023, 9, 201. DOI
               9.  Gong, X.; Louie, S. G.; Duan, W.; Xu, Y. Generalizing deep learning electronic structure calculation to the plane-wave basis. Nat.
                  Comput. Sci. 2024, 4, 752-60. DOI PubMed PMC
               10.  Dang, L.; He, X.; Tang, D.; Xin, H.; Wu, B. A fatigue life prediction framework of laser-directed energy deposition Ti-6Al-4V based on
                  physics-informed neural network. IJSI 2025, 16, 327-54. DOI
               11.  Rasul, A.; Karuppanan, S.; Perumal, V.; Ovinis, M.; Iqbal, M. An artificial neural network model for determining stress concentration
                  factors for fatigue design of tubular T-joint under compressive loads. IJSI 2024, 15, 633-52. DOI
               12.  Yuan, R.; Wang, B.; Li, J.; et al. Machine learning-enabled design of ferroelectrics with multiple properties via a Landau model. Acta.
                  Mater. 2025, 286, 120760. 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.  Butler, K. T.; Davies, D. W.; Cartwright, H.; Isayev, O.; Walsh, A. Machine learning for molecular and materials science. Nature 2018,
                  559, 547-55. DOI PubMed
               15.  Raccuglia, P.; Elbert, K. C.; Adler, P. D.; et al. Machine-learning-assisted materials discovery using failed experiments. Nature 2016,
                  533, 73-6. DOI PubMed
               16.  Dinic, F.; Singh, K.; Dong, T.; et al. Applied machine learning for developing next‐generation functional materials. Adv. Funct. Mater.
                  2021, 31, 2104195. DOI
               17.  Rickman, J.; Lookman, T.; Kalinin, S. Materials informatics: from the atomic-level to the continuum. Acta. Mater. 2019, 168, 473-510.
                  DOI
               18.  Hong, T.; Chen, T.; Jin, D.; et al. Discovery of new topological insulators and semimetals using deep generative models. npj. Quantum.
                  Mater. 2025, 10, 12. DOI
               19.  Xie, J. Prospects of materials genome engineering frontiers. Mater. Genome. Eng. Adv. 2023, 1, e17. DOI
               20.  Jiang, X.; Fu, H.; Bai, Y.; et al. Interpretable machine learning applications: a promising prospect of AI for materials. Adv. Funct. Mater.
                  2025, 35, 2507734. DOI
               21.  Hart, G. L. W.; Mueller, T.; Toher, C.; Curtarolo, S. Machine learning for alloys. Nat. Rev. Mater. 2021, 6, 730-55. DOI
               22.  Hippalgaonkar, K.; Li, Q.; Wang, X.; Fisher, J. W.; Kirkpatrick, J.; Buonassisi, T. Knowledge-integrated machine learning for materials:
                  lessons from gameplaying and robotics. Nat. Rev. Mater. 2023, 8, 241-60. DOI
               23.  Tabor, D. P.; Roch, L. M.; Saikin, S. K.; et al. Accelerating the discovery of materials for clean energy in the era of smart automation.
                  Nat. Rev. Mater. 2018, 3, 5-20. DOI
               24.  Gong, X.; Li, H.; Zou, N.; Xu, R.; Duan, W.; Xu, Y. General framework for E(3)-equivariant neural network representation of density
                  functional theory Hamiltonian. Nat. Commun. 2023, 14, 2848. DOI PubMed PMC
               25.  Li, H.; Wang, Z.; Zou, N.; et al. Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure
                  calculation. Nat. Comput. Sci. 2022, 2, 367-77. DOI PubMed PMC
               26.  Cohen, A. J.; Mori-Sánchez, P.; Yang, W. Challenges for density functional theory. Chem. Rev. 2012, 112, 289-320. DOI PubMed
               27.  Janesko, B. G. Replacing hybrid density functional theory: motivation and recent advances. Chem. Soc. Rev. 2021, 50, 8470-95. DOI
                  PubMed
               28.  Takeno, S.; Tsukada, Y.; Fukuoka, H.; Koyama, T.; Shiga, M.; Karasuyama, M. Cost-effective search for lower-error region in material
                  parameter space using multifidelity Gaussian process modeling. Phys. Rev. Materials. 2020, 4, 083802. DOI
               29.  Chen, C.; Zuo, Y.; Ye, W.; Li, X.; Ong, S. P. Learning properties of ordered and disordered materials from multi-fidelity data. Nat.
                  Comput. Sci. 2021, 1, 46-53. DOI PubMed
               30.  Lu, L.; Dao, M.; Kumar, P.; Ramamurty, U.; Karniadakis, G. E.; Suresh, S. Extraction of mechanical properties of materials through
                  deep learning from instrumented indentation. Proc. Natl. Acad. Sci. U. S. A. 2020, 117, 7052-62. DOI PubMed PMC
               31.  Jain, A.; Hautier, G.; Moore, C. J.; et al. A high-throughput infrastructure for density functional theory calculations. Comput. Mater. Sci.
                  2011, 50, 2295-310. DOI
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