Page 106 - Read Online
P. 106

Page 30 of 31                       Shu et al. J. Mater. Inf. 2025, 5, 36  https://dx.doi.org/10.20517/jmi.2025.13

               127.      Wang, H.; Ouyang, R.; Chen, W.; Pasquarello, A. High-quality data enabling universality of band gap descriptor and discovery of
                    photovoltaic perovskites. J. Am. Chem. Soc. 2024, 146, 17636-45.  DOI  PubMed
               128.      Kim, J.; Noh, J.; Im, J. Machine learning-enabled chemical space exploration of all-inorganic perovskites for photovoltaics. npj.
                    Comput. Mater. 2024, 10, 1270.  DOI
               129.      Mahal, E.; Roy, D.; Manna, S. S.; Pathak, B. Machine learning-driven prediction of band-alignment types in 2D hybrid perovskites. J.
                    Mater. Chem. A. 2023, 11, 23547-55.  DOI
               130.      Nayak, P. K.; Mora Perez, C.; Liu, D.; Prezhdo, O. V.; Ghosh, D. A-cation-dependent excited state charge carrier dynamics in
                    vacancy-ordered halide perovskites: insights from computational and machine learning models. Chem. Mater. 2024, 36, 3875-85.
                    DOI
               131.      Wang, S.; Yousefi Amin, A. A.; Wu, L.; Cao, M.; Zhang, Q.; Ameri, T. Perovskite nanocrystals: synthesis, stability, and
                    optoelectronic applications. Small. Struct. 2021, 2, 2000124.  DOI
               132.      Liu, J.; Yang, Z.; Ye, B.; et al. A review of stability-enhanced luminescent materials: fabrication and optoelectronic applications. J.
                    Mater. Chem. C. 2019, 7, 4934-55.  DOI
               133.      Liu, H.; Cheng, J.; Dong, H.; et al. Screening stable and metastable ABO  perovskites using machine learning and the materials
                                                                     3
                    project. Comput. Mater. Sci. 2020, 177, 109614.  DOI
               134.      Burlingame, Q.; Ball, M.; Loo, Y. It’s time to focus on organic solar cell stability. Nat. Energy. 2020, 5, 947-9.  DOI
               135.      Bartel, C. J.; Sutton, C.; Goldsmith, B. R.; et al. New tolerance factor to predict the stability of perovskite oxides and halides. Sci.
                    Adv. 2019, 5, eaav0693.  DOI  PubMed  PMC
               136.      Gu, G. H.; Jang, J.; Noh, J.; Walsh, A.; Jung, Y. Perovskite synthesizability using graph neural networks. npj. Comput. Mater. 2022,
                    8, 757.  DOI
               137.      Fu, Y.; Zhu, H.; Chen, J.; Hautzinger, M. P.; Zhu, X.; Jin, S. Metal halide perovskite nanostructures for optoelectronic applications
                    and the study of physical properties. Nat. Rev. Mater. 2019, 4, 169-88.  DOI
               138.      Li, J.; Duan, J.; Yang, X.; Duan, Y.; Yang, P.; Tang, Q. Review on recent progress of lead-free halide perovskites in optoelectronic
                    applications. Nano. Energy. 2021, 80, 105526.  DOI
               139.      Cai, X.; Li, Y.; Liu, J.; Zhang, H.; Pan, J.; Zhan, Y. Discovery of all-inorganic lead-free perovskites with high photovoltaic
                    performance via ensemble machine learning. Mater. Horiz. 2023, 10, 5288-97.  DOI
               140.      Liu, Z.; Rolston, N.; Flick, A. C.; et al. Machine learning with knowledge constraints for process optimization of open-air perovskite
                    solar cell manufacturing. Joule 2022, 6, 834-49.  DOI
               141.      Chen, T.; Pang, Z.; He, S.; et al. Machine intelligence-accelerated discovery of all-natural plastic substitutes. Nat. Nanotechnol. 2024,
                    19, 782-91.  DOI  PubMed  PMC
               142.      Mai, H.; Le, T. C.; Chen, D.; Winkler, D. A.; Caruso, R. A. Machine learning for electrocatalyst and photocatalyst design and
                    discovery. Chem. Rev. 2022, 122, 13478-515.  DOI
               143.      Osman, A. I.; Nasr, M.; Eltaweil, A. S.; et al. Advances in hydrogen storage materials: harnessing innovative technology, from
                    machine learning to computational chemistry, for energy storage solutions. Int. J. Hydrogen. Energy. 2024, 67, 1270-94.  DOI
               144.      Ma, X. Y.; Lewis, J. P.; Yan, Q. B.; Su, G. Accelerated discovery of two-dimensional optoelectronic octahedral oxyhalides via high-
                    throughput ab initio calculations and machine learning. J. Phys. Chem. Lett. 2019, 10, 6734-40.  DOI  PubMed
               145.      Jin, H.; Zhang, H.; Li, J.; et al. Discovery of novel two-dimensional photovoltaic materials accelerated by machine learning. J. Phys.
                    Chem. Lett. 2020, 11, 3075-81.  DOI
               146.      Wang, Z.; Zhang, H.; Li, J. Accelerated discovery of stable spinels in energy systems via machine learning. Nano. Energy. 2021, 81,
                    105665.  DOI
               147.      Alibagheri, E.; Ranjbar, A.; Khazaei, M.; Kühne, T. D.; Vaez Allaei, S. M. Remarkable optoelectronic characteristics of
                    synthesizable square-octagon haeckelite structures: machine learning materials discovery. Adv. Funct. Mater. 2024, 34, 2402390.
                    DOI
               148.      Li, Y.; Yang, J.; Zhao, R.; et al. Design of organic-inorganic hybrid heterostructured semiconductors via high-throughput materials
                    screening for optoelectronic applications. J. Am. Chem. Soc. 2022, 144, 16656-66.  DOI  PubMed
               149.      Chen, J.; Xu, W.; Zhang, R. Δ-Machine learning-driven discovery of double hybrid organic-inorganic perovskites. J. Mater. Chem. A.
                    2022, 10, 1402-13.  DOI
               150.      Chen, A.; Wang, Z.; Gao, J.; et al. A data-driven platform for two-dimensional hybrid lead-halide perovskites. ACS. Nano. 2023, 17,
                    13348-57.  DOI
               151.      Liu, Y.; Madanchi, A.; Anker, A. S.; Simine, L.; Deringer, V. L. The amorphous state as a frontier in computational materials design.
                    Nat. Rev. Mater. 2025, 10, 228-41.  DOI
               152.      Merchant, A.; Batzner, S.; Schoenholz, S. S.; Aykol, M.; Cheon, G.; Cubuk, E. D. Scaling deep learning for materials discovery.
                    Nature 2023, 624, 80-5.  DOI  PubMed  PMC
               153.      Szymanski, N. J.; Rendy, B.; Fei, Y.; et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 2023,
                    624, 86-91.  DOI  PubMed  PMC
               154.      Zeni, C.; Pinsler, R.; Zügner, D.; et al. A generative model for inorganic materials design. Nature 2025, 639, 624-32.  DOI  PubMed
                    PMC
               155.      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
   101   102   103   104   105   106   107   108   109   110   111