Wang et al. J Mater Inf 2022;2:1 DOI: 10.20517/jmi.2021.11 Journal of Materials Informatics © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. www.jmijournal.com Open Access Review Big data-assisted digital twins for the smart design and manufacturing of advanced materials: from atoms to products William-Yi Wang 1,7 , Junlei Yin 1 , Zaixian Chai 1 , Xin Chen 2 , Wenping Zhao 3 , Jiaqi Lu 1 , Feng Sun 4 , Qinggong Jia 4,8 , Xingyu Gao 2 , Bin Tang 1,7 , Xidong Hui 5 , Haifeng Song 2 , Fei Xue 1 , Zi-Kui Liu 6 , Jinshan Li 1,7 1 State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China. 2 CAEP Software Center for High Performance Numerical Simulation & Institute of Applied Physics and Computational Mathematics, Beijing 100088, China. 3 CRRC Tangshan Co., LTD, Tangshan 063035, Hebei, China. 4 Western Superconducting Technologies Co., Ltd., Xi’an 710018, Shaanxi, China. 5 State Key Laboratory for Advanced Metals and Materials, University of Science and Technology Beijing, Beijing 100083, China. 6 Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USA. 7 Innovation Center, NPU Chongqing, Chongqing 401135, China. 8 School of Materials Science and Engineering, Xi’an University of Technology, Xi’an 710048, Shaanxi, China. Correspondence to: Jinshan Li, State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China. E-mail: ljsh@nwpu.edu.cn; Haifeng Song, CAEP Software Center for High Performance Numerical Simulation & Institute of Applied Physics and Computational Mathematics, Beijing 100088, China. E-mail: song_haifeng@iapcm.ac.cn; Fei Xue, State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China. E-mail: 13913573200@139.com How to cite this article: Wang WY, Yin J, Chai Z, Chen X, Zhao W, Lu J, Sun F, Jia Q, Gao X, Tang B, Hui X, Song H, Xue F, Liu ZK, Li J. Big data-assisted digital twins for the smart design and manufacturing of advanced materials: from atoms to products. J Mater Inf 2022;2:1. https://dx.doi.org/10.20517/jmi.2021.11 Received: 2 Nov 2021 First Decision: 27 Dec 2021 Revised: 11 Jan 2022 Accepted: 10 Feb 2022 Published: 23 Feb 2022 Academic Editors: Xingjun Liu, Tong-Yi Zhang Copy Editor: Xi-Jun Chen Production Editor: Xi-Jun Chen Abstract Motivated by the ever-increasing wealth of data boosted by national strategies in terms of data-driven Integrated Computational Materials Engineering (ICME), Materials Genome Engineering, Materials Genome Infrastructures, Industry 4.0, Materials 4.0 and so on, materials informatics represents a unique strategy in revealing the fundamental relationships in the development and manufacturing of advanced materials. Materials developments are becoming ever more integrated with robust data-driven and data-intensive technologies. In the present review, big data-assisted digital twins (DTs) for the smart design and manufacturing of advanced materials are presented from the perspective of the digital thread. In the introduction of the DT design paradigm in the ICME era, the