Computational modeling of grain boundary segregation: A review

Hu, Chongze; Dingreville, Rémi Philippe Michel; Boyce, Brad L.

doi:10.1016/j.commatsci.2023.112596

Computational modeling of grain boundary segregation: A review

Journal Article · Sun Nov 05 23:00:00 EST 2023 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2023.112596· OSTI ID:2311370

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Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States). Center for Integrated Nanotechnologies (CINT); Univ. of Alabama, Tuscaloosa, AL (United States)
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States). Center for Integrated Nanotechnologies (CINT)

Nearly all metals, alloys, ceramics, and their associated composites are polycrystalline in nature, with grain boundaries that separate well-defined crystalline regions that influence materials properties. In all but the most pure elemental systems, intentional solutes or impurities are present and can segregate to, or less commonly away from, the grain boundaries, in turn influencing boundary behavior, their stability, and associated materials properties. In some cases, grain-boundary segregation can also trigger “phase-like” structural transitions that dramatically alter the essential nature of the boundary. With the development of advanced electron microscopy techniques, researchers can directly observe grain-boundary structures and segregation with atomic precision. Despite such spatial resolution, the underlying mechanisms governing grain-boundary segregation remain difficult to characterize. As a result, computational modeling techniques such as density functional theory, molecular dynamics, mesoscale phase-field, continuum defect theory, and others are important complementary tools to experimental observations for studying grain-boundary segregation behavior. In conclusion, these computational methods offer the ability to explore the underlying formation mechanisms of grain-boundary segregation, elucidate complex segregation behavior, and provide insights into solutions to effectively controlling microstructure.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE)

Grant/Contract Number:: NA0003525

OSTI ID:: 2311370

Report Number(s):: SAND--2023-13724J

Journal Information:: Computational Materials Science, Journal Name: Computational Materials Science Journal Issue: 1 Vol. 232; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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