iamrp.net-O3yM-1791491023-y707593740 1791491023 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 2023 2 1 Sanjay Kulkarni Meenal Joshi Rohan Patil Aniket Deshmukh 2023 10.68159/y707593740 https://iamrp.net/pub/journal/2/article/y707593740 Hu J, Liu D, Fu N, Dong R. Improving realistic material property prediction using domain adaptation based machine learning [Preprint]. arXiv; 2023. arXiv:2308.02937. Goetz A, Durmaz AR, Müller M, Thomas A, Britz D, Kerfriden P, et al. Addressing materials' microstructure diversity using transfer learning. NPJ Comput Mater. 2022;8(1):27. 10.1038/s41524-022-00703-z Jha D, Choudhary K, Tavazza F, Liao WK, Choudhary A, Campbell C, et al. Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning. Nat Commun. 2019;10(1):5316. 10.1038/s41467-019-13297-w Kong S, Guevarra D, Gomes CP, Gregoire JM. Materials representation and transfer learning for multi-property prediction. Appl Phys Rev. 2021;8(2):021409. 10.1063/5.0047066 Feng S, Zhou H, Dong H. Application of deep transfer learning to predicting crystal structures of inorganic substances. Comput Mater Sci. 2021;195:110476. 10.1016/j.commatsci.2021.110476 Hoffmann N, Schmidt J, Botti S, Marques MAL. Transfer learning on large datasets for the accurate prediction of material properties. Digit Discov. 2023;2(5):1368-79. 10.1039/D3DD00030C Ford E, Maneparambil K, Kumar A, Sant G, Neithalath N. Transfer (machine) learning approaches coupled with target data augmentation to predict the mechanical properties of concrete. Mach Learn Appl. 2022;8:100271. 10.1016/j.mlwa.2022.100271 Sun H, Zhang H, Ren G, Zhang C. A knowledge transfer framework for general alloy materials properties prediction. Materials (Basel). 2022;15(21):7442. 10.3390/ma15217442 Jiang L, Zhang Z, Hu H, He X, Fu H, Xie J. A rapid and effective method for alloy materials design via sample data transfer machine learning. NPJ Comput Mater. 2023;9(1):26. 10.1038/s41524-023-00979-9 Lee J, Asahi R. Transfer learning for materials informatics using crystal graph convolutional neural network. Comput Mater Sci. 2021;190:110314. 10.1016/j.commatsci.2021.110314 Axelrod S, Schwalbe-Koda D, Mohapatra S, Damewood J, Greenman KP, Gómez-Bombarelli R. Learning matter: Materials design with machine learning and atomistic simulations. Acc Mater Res. 2022;3(3):343-57. 10.1021/accountsmr.1c00238 Batra R, Song L, Ramprasad R. Emerging materials intelligence ecosystems propelled by machine learning. Nat Rev Mater. 2021;6(8):655-78. 10.1038/s41578-020-00255-y Bartók AP, De S, Poelking C, Bernstein N, Kermode JR, Csányi G, et al. Machine learning unifies the modeling of materials and molecules. Sci Adv. 2017;3(12). 10.1126/sciadv.1701816 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 Zhao X, Shao F, Zhang Y. A novel joint adversarial domain adaptation method for rotary machine fault diagnosis under different working conditions. Sensors (Basel). 2022;22(22):9007. 10.3390/s22229007 Parvez MR, Chang KW. Evaluating the values of sources in transfer learning. In: Proceedings of the 2021 conference of the North American chapter of the Association for Computational Linguistics: Human language technologies; 2021 Jun 6-11; Online. Stroudsburg (PA): Association for Computational Linguistics; 2021. p. 5084-116. 10.18653/v1/2021.naacl-main.402 Wong LJ, McPherson S, Michaels AJ. Assessing the value of transfer learning metrics for RF domain adaptation [Preprint]. arXiv; 2022. arXiv:2206.08329. 10.48550/arXiv.2206.08329 Chatterjee M, Roy K. Chemical similarity and machine learning-based approaches for the prediction of aquatic toxicity of binary and multicomponent pharmaceutical and pesticide mixtures against Aliivibrio fischeri. Chemosphere. 2022;308(Pt 3):136463. 10.1016/j.chemosphere.2022.136463 Skinnider MA, Dejong CA, Franczak BC, McNicholas PD, Magarvey NA. Comparative analysis of chemical similarity methods for modular natural products with a hypothetical structure enumeration algorithm. J Cheminform. 2017;9(1):46. 10.1186/s13321-017-0234-y Wu F, Courty N, Jin S, Li SZ. Improving molecular representation learning with metric learning-enhanced optimal transport. Patterns (N Y). 2023;4(4):100714. 10.1016/j.patter.2023.100714 Wang Z, Goetz J, Brenning A. Transfer learning for landslide susceptibility modeling using domain adaptation and case-based reasoning. Geosci Model Dev. 2022;15(23):8765-84. 10.5194/gmd-15-8765-2022 Liu F, Liu Q, Bannur S, Pérez-García F, Usuyama N, Zhang S, et al. Compositional zero-shot domain transfer with text-to-text models. Trans Assoc Comput Linguist. 2023;11:1097-113. 10.1162/tacl_a_00585 Park K, Ko YJ, Durai P, Pan CH. Machine learning-based chemical binding similarity using evolutionary relationships of target genes. Nucleic Acids Res. 2019;47(20). 10.1093/nar/gkz743 Wang HC, Botti S, Marques MAL. Predicting stable crystalline compounds using chemical similarity. NPJ Comput Mater. 2021;7(1):12. 10.1038/s41524-020-00481-6 Hwang J, Iwasaki Y. Improving efficiency of autonomous material search via transfer learning from nontarget properties. Sci Technol Adv Mater Methods. 2023;3(1):2254202. 10.1080/27660400.2023.2254202 Zhu J, Zhang X, Guo M, Li J, Hu J, Cai S, et al. Restructured single parabolic band model for quick analysis in thermoelectricity. NPJ Comput Mater. 2021;7(1):116. 10.1038/s41524-021-00587-5 Chen C, Ong SP. AtomSets as a hierarchical transfer learning framework for small and large materials datasets. NPJ Comput Mater. 2021;7(1):173. 10.1038/s41524-021-00639-w Xu Q, Shen S, de Souza RS, Chen M, Ye R, She Y, et al. From images to features: Unbiased morphology classification via variational auto-encoders and domain adaptation. Mon Not R Astron Soc. 2023;526(4):6391-400. 10.1093/mnras/stad3181 Gupta V, Choudhary K, Mao Y, Wang K, Tavazza F, Campbell C, et al. MPpredictor: An artificial intelligence-driven web tool for composition-based material property prediction. J Chem Inf Model. 2023;63(7):1865-71. 10.1021/acs.jcim.3c00307