A brief review of data-driven ICME for intelligently discovering advanced structural metal materials: Insight into atomic and electronic building blocks. Issue 8 (28th April 2020)

• Record Type: Journal Article
• Authors: Wang, William Yi; Tang, Bin; Lin, Deye; Zou, Chengxiong; Zhang, Ying; Shang, Shun-Li; Guan, Quanmei; Gao, Jun; Fan, Letian; Kou, Hongchao; Song, Haifeng; Ma, Jijun; Hui, Xi-Dong; Gao, Michael C.; Liu, Zi-Kui; Li, Jinshan
• Is Part Of: Journal of materials research. Volume 35:Issue 8(2020)
• Page Start: 872
• Page End: 889
• Publication Date: 2020-04-28
• Subjects: defects, -- electronic structures, -- simulation; Materials -- Research -- Periodicals; 620.1105
• DOI: 10.1557/jmr.2020.43
• Languages: English
• ISSNs: 0884-2914

Abstract

Abstract: Abstract : This article presents a brief review of our case studies of data-driven Integrated Computational Materials Engineering (ICME) for intelligently discovering advanced structural metal materials, including light-weight materials (Ti, Mg, and Al alloys), refractory high-entropy alloys, and superalloys. The basic bonding in terms of topology and electronic structures is recommended to be considered as the building blocks/units constructing the microstructures of advanced materials. It is highlighted that the bonding charge density could not only provide an atomic and electronic insight into the physical nature of chemical bond of materials but also reveal the fundamental strengthening/embrittlement mechanisms and the local phase transformations of planar defects, paving a path in accelerating the development of advanced metal materials via interfacial engineering. Perspectives on the knowledge-based modeling/simulations, machine-learning knowledge base, platform, and next-generation workforce for sustainable ecosystem of ICME are highlighted, thus to call for more duty on the developments of advanced structural metal materials and enhancement of research productivity and collaboration.