A brief review of data-driven ICME for intelligently discovering advanced structural metal materials: Insight into atomic and electronic building blocks

William Yi Wang, Bin Tang, Deye Lin, Chengxiong Zou, Ying Zhang, Shun Li Shang, Quanmei Guan, Jun Gao, Letian Fan, Hongchao Kou, Haifeng Song, Jijun Ma, Xi Dong Hui, Michael C. Gao, Zi Kui Liu, Jinshan Li

Research output: Contribution to journalReview articlepeer-review

23 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)872-889
Number of pages18
JournalJournal of Materials Research
Volume35
Issue number8
DOIs
StatePublished - Apr 28 2020

All Science Journal Classification (ASJC) codes

  • General Materials Science
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering

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