iamrp.net-gD2G-1791491020-h197852185 1791491020 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 1 Sven Larsson Erik Johansson Anna Nilsson 2024 10.68159/h197852185 https://iamrp.net/pub/journal/2/article/h197852185 Harshvardhan GM, Gourisaria MK, Pandey M, Rautaray SS. A comprehensive survey and analysis of generative models in machine learning. Comput Sci Rev. 2020;38:100285. 10.1016/j.cosrev.2020.100285 Ruthotto L, Haber E. An introduction to deep generative modeling. GAMM Mitt. 2021;44(2). 10.1002/gamm.202100008 Oussidi A, Elhassouny A. Deep generative models: Survey. In: Int Conf Intell Syst Comput Vis. Piscataway (NJ): IEEE; 2018. p. 1-8. 10.1109/ISACV.2018.8354080 Chen L, Zhang W, Nie Z, Li S, Pan F. Generative models for inverse design of inorganic solid materials. J Mater Inform. 2021;1(1):4. 10.20517/jmi.2021.07 Kim S, Noh J, Gu GH, Aspuru-Guzik A, Jung Y. Generative adversarial networks for crystal structure prediction. ACS Cent Sci. 2020;6(8):1412-20. 10.1021/acscentsci.0c00426 Long T, Fortunato NM, Opahle I, Zhang Y, Samathrakis I, Shen C, et al. Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures. NPJ Comput Mater. 2021;7(1):66. 10.1038/s41524-021-00526-4 Noh J, Gu GH, Kim S, Jung Y. Machine-enabled inverse design of inorganic solid materials: Promises and challenges. Chem Sci. 2020;11(19):4871-81. 10.1039/D0SC00594K Ma W, Cheng F, Xu Y, Wen Q, Liu Y. Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy. Adv Mater. 2019;31(35):1901111. 10.1002/adma.201901111 Bastek JH, Kochmann DM. Inverse design of nonlinear mechanical metamaterials via video denoising diffusion models. Nat Mach Intell. 2023;5(12):1466-75. 10.1038/s42256-023-00762-x Zeni C, Pinsler R, Zügner D, Fowler A, Horton M, Fu X, et al. MatterGen: A generative model for inorganic materials design. arXiv. 2023;2312.03687. 10.48550/arXiv.2312.03687 Lu S, Zhou Q, Chen X, Song Z, Wang J. Inverse design with deep generative models: Next step in materials discovery. Natl Sci Rev. 2022;9(8). 10.1093/nsr/nwac111 Zhang Q, Tao M, Chen Y. gDDIM: Generalized denoising diffusion implicit models. arXiv. 2022;2206.05564. 10.48550/arXiv.2206.05564 Lalena JN, Cleary DA, Duparc OB. Principles of inorganic materials design. 3rd ed. Hoboken: John Wiley & Sons; 2020. Liu Z, Zhu D, Rodrigues SP, Lee KT, Cai W. Generative model for the inverse design of metasurfaces. Nano Lett. 2018;18(10):6570-6. 10.1021/acs.nanolett.8b03171 Sánchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: Generative models for matter engineering. Science. 2018;361(6400):360-5. 10.1126/science.aat2663 Zhong C, Zhang J, Lu X, Zhang K, Liu J, Hu K, et al. Deep generative model for inverse design of high-temperature superconductor compositions with predicted Tc > 77 K. ACS Appl Mater Interfaces. 2023;15(25):30029-38. 10.1021/acsami.3c00593 Cai H, Srinivasan S, Czaplewski DA, Martinson AB, Gosztola DJ, Stan L, et al. Inverse design of metasurfaces with non-local interactions. NPJ Comput Mater. 2020;6(1):116. 10.1038/s41524-020-00369-5 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 Preuer K, Renz P, Unterthiner T, Hochreiter S, Klambauer G. Fréchet ChemNet distance: A metric for generative models for molecules in drug discovery. J Chem Inf Model. 2018;58(9):1736-41. 10.1021/acs.jcim.8b00234 Betzalel E, Penso C, Navon A, Fetaya E. A study on the evaluation of generative models. arXiv. 2022;2206.10935. 10.48550/arXiv.2206.10935 Handa K, Thomas MC, Kageyama M, Iijima T, Bender A. On the difficulty of validating molecular generative models realistically: A case study on public and proprietary data. J Cheminform. 2023;15(1):112. 10.1186/s13321-023-00781-1 Xu Q, Huang G, Yuan Y, Guo C, Sun Y, Wu F, et al. An empirical study on evaluation metrics of generative adversarial networks. arXiv. 2018;1806.07755. 10.48550/arXiv.1806.07755 Naeem MF, Oh SJ, Uh Y, Choi Y, Yoo J. Reliable fidelity and diversity metrics for generative models. In: Proc Mach Learn Res. 2020;119:7176-85. Bilodeau C, Jin W, Jaakkola T, Barzilay R, Jensen KF. Generative models for molecular discovery: Recent advances and challenges. Wiley Interdiscip Rev Comput Mol Sci. 2022;12(5). 10.1002/wcms.1608 Dollar O, Joshi N, Beck DA, Pfaendtner J. Attention-based generative models for de novo molecular design. Chem Sci. 2021;12(24):8362-72. 10.1039/D1SC01050F Ren Z, Tian SIP, Noh J, Oviedo F, Xing G, Li J, et al. An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties. Matter. 2022;5(1):314-35. 10.1016/j.matt.2021.11.032 Yao Z, Sánchez-Lengeling B, Bobbitt NS, Bucior BJ, Kumar SGH, Collins SP, et al. Inverse design of nanoporous crystalline reticular materials with deep generative models. Nat Mach Intell. 2021;3(1):76-86. 10.1038/s42256-020-00271-1 Bian Y, Xie XQ. Generative chemistry: Drug discovery with deep learning generative models. J Mol Model. 2021;27(3):71. 10.1007/s00894-021-04674-8 Mehmood R, Bashir R, Giri KJ. Deep generative models: A review. Indian J Sci Technol. 2023;16(7):460-7. 10.17485/IJST/v16i7.2296 Vlassis NN, Sun W. Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties. Comput Methods Appl Mech Eng. 2023;413:116126. 10.1016/j.cma.2023.116126 Wang J, Li R, He C, Chen H, Cheng R, Zhai C, et al. An inverse design method for supercritical airfoil based on conditional generative models. Chin J Aeronaut. 2022;35(3):62-74. 10.1016/j.cja.2021.03.006 Medina E, Farrell PE, Bertoldi K, Rycroft CH. Navigating the landscape of nonlinear mechanical metamaterials for advanced programmability. Phys Rev B. 2020;101(6):064101. 10.1103/PhysRevB.101.064101 Maiorca A, Bohy H, Yoon Y, Dutoit T. Objective evaluation metric for motion generative models: Validating Fréchet Motion Distance on foot skating and over-smoothing artifacts. In: Proc ACM SIGGRAPH Conf Motion Interact Games. 2023. p. 1-11. 10.1145/3623264.3624443 Duan Y, Hong Y, Niu L, Zhang L. Few-shot defect image generation via defect-aware feature manipulation. Proc AAAI Conf Artif Intell. 2023;37(1):571-8. 10.1609/aaai.v37i1.25132