iamrp.net-SEu1-1791491022-w706175094 1791491022 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Computational and Data-Driven Materials Engineering J. Comput. Data-Driven Mater. Eng. 3149-9368 2024 3 2 Victor Santos Rafael Costa Bruno Teixeira 2024 10.68159/w706175094 https://iamrp.net/pub/journal/2/article/w706175094 Xie T, Grossman JC. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett. 2018;120(14):145301. 10.1103/PhysRevLett.120.145301 Chen C, Ye W, Zuo Y, Zheng C, Ong SP. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater. 2019;31(9):3564-72. 10.1021/acs.chemmater.9b01294 Qian C, Xiong Y, Chen X. Directed graph attention neural network utilizing 3D coordinates for molecular property prediction. Comput Mater Sci. 2021;200:110761. 10.1016/j.commatsci.2021.110761 Li S, Zhou J, Xu T, Huang L, Wang F, Xiong H, et al. Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining; 2021 Aug 14-18; Virtual Event, Singapore. New York: Association for Computing Machinery; 2021. p. 975-85. 10.1145/3447548.3467311 Corso G, Stark H, Jegelka S, Jaakkola T, Barzilay R. Graph neural networks. Nat Rev Methods Primers. 2024;4(1):17. 10.1038/s43586-024-00294-7 Wieder O, Kohlbacher S, Kuenemann M, Garon A, Ducrot P, Seidel T, et al. A compact review of molecular property prediction with graph neural networks. Drug Discov Today Technol. 2020;37:1-12. 10.1016/j.ddtec.2020.11.009 Anstine DM, Isayev O. Machine learning interatomic potentials and long-range physics. J Phys Chem A. 2023;127(11):2417-31. 10.1021/acs.jpca.2c06778 Batzner S, Musaelian A, Sun L, Geiger M, Mailoa JP, Kornbluth M, et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat Commun. 2022;13(1):2453. 10.1038/s41467-022-29939-5 Ouyang X, Zhuang Y, Zhang J, Zhang F, Jie X, Chen W, et al. Quantum-accurate modeling of ferroelectric phase transition in perovskites from message-passing neural networks. J Phys Chem C. 2023;127(42):20890-902. 10.1021/acs.jpcc.3c04888 Nazarova AL, Yang L, Liu K, Mishra A, Kalia RK, Nomura KI, et al. Dielectric polymer property prediction using recurrent neural networks with optimizations. J Chem Inf Model. 2021;61(5):2175-86. 10.1021/acs.jcim.0c01366 Balachandran PV, Kowalski B, Sehirlioglu A, Lookman T. Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning. Nat Commun. 2018;9(1):1668. 10.1038/s41467-018-03821-9 He J, Li J, Liu C, Wang C, Zhang Y, Wen C, et al. Machine learning identified materials descriptors for ferroelectricity. Acta Mater. 2021;209:116815. 10.1016/j.actamat.2021.116815 Fang Z, Yan Q. Towards accurate prediction of configurational disorder properties in materials using graph neural networks. NPJ Comput Mater. 2024;10(1):91. 10.1038/s41524-024-01283-w Pandey S, Qu J, Stevanović V, St John P, Gorai P. Predicting energy and stability of known and hypothetical crystals using graph neural network. Patterns (N Y). 2021;2(11):100361. 10.1016/j.patter.2021.100361 Schütt KT, Sauceda HE, Kindermans PJ, Tkatchenko A, Müller KR. SchNet – A deep learning architecture for molecules and materials. J Chem Phys. 2018;148(24):241722. 10.1063/1.5019779 Park CW, Wolverton C. Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery. Phys Rev Mater. 2020;4(6):063801. 10.1103/PhysRevMaterials.4.063801 Satorras VG, Hoogeboom E, Welling M. E(n) equivariant graph neural networks. In: Proceedings of the 38th International Conference on Machine Learning; 2021 Jul 18-24. PMLR; 2021. p. 9323-32. Proceedings of Machine Learning Research; vol. 139. Kozin VK, Kyriienko O. Quantum time crystals from Hamiltonians with long-range interactions. Phys Rev Lett. 2019;123(21):210602. 10.1103/PhysRevLett.123.210602 Mao Z, Li W, Tan J. Dielectric tensor prediction for inorganic materials using latent information from preferred potential. NPJ Comput Mater. 2024;10(1):265. 10.1038/s41524-024-01450-z Bernstein N, Csányi G, Deringer VL. De novo exploration and self-guided learning of potential-energy surfaces. NPJ Comput Mater. 2019;5(1):99. 10.1038/s41524-019-0236-6 Gong S, Yan K, Xie T, Shao-Horn Y, Gomez-Bombarelli R, Ji S, et al. Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity. Sci Adv. 2023;9(45):eadi3245. 10.1126/sciadv.adi3245 Kosmala A, Gasteiger J, Gao N, Günnemann S. Ewald-based long-range message passing for molecular graphs. In: Proceedings of the 40th International Conference on Machine Learning; 2023 Jul 23-29. PMLR; 2023. p. 17544-63. Proceedings of Machine Learning Research; vol. 202. Lin Y, Yan K, Luo Y, Liu Y, Qian X, Ji S. Efficient approximations of complete interatomic potentials for crystal property prediction. In: Proceedings of the 40th International Conference on Machine Learning; 2023 Jul 23-29. PMLR; 2023. p. 21260-87. Proceedings of Machine Learning Research; vol. 202. Wang Y, Cheng C, Li S, Ren Y, Shao B, Liu G, et al. Neural P3M: A long-range interaction modeling enhancer for geometric GNNs. Adv Neural Inf Process Syst. 2024;37:120336-65. Sanchez-Gonzalez A, Bapst V, Cranmer K, Battaglia P. Hamiltonian graph networks with ODE integrators. arXiv [Preprint]. 2019. 10.48550/arXiv.1909.12790 Wang J, Gao H, Han Y, Ding C, Pan S, Wang Y, et al. MAGUS: machine learning and graph theory assisted universal structure searcher. Natl Sci Rev. 2023;10(7):nwad128. 10.1093/nsr/nwad128 Seko A, Hayashi H, Kashima H, Tanaka I. Matrix- and tensor-based recommender systems for the discovery of currently unknown inorganic compounds. Phys Rev Mater. 2018;2(1):013805. 10.1103/PhysRevMaterials.2.013805 Thong HC, Wang X, Han J, Zhang L, Li B, Wang K, et al. Machine learning interatomic potential for molecular dynamics simulation of the ferroelectric KNbO3 perovskite. Phys Rev B. 2023;107(1):014101. 10.1103/PhysRevB.107.014101