A machine learning approach to model solute grain boundary segregation. (December 2018)

• Record Type: Journal Article
• Authors: Huber, Liam; Hadian, Raheleh; Grabowski, Blazej; Neugebauer, Jörg
• Is Part Of: Npj computational materials. Volume 4:issue 1(2018)
• Page Start: 1
• Page End: 8
• Publication Date: 2018-12
• Subjects: Materials science -- Computer simulation -- Periodicals; Materials science -- Mathematical models -- Periodicals; Materials science -- Computer simulation; Electronic journals; Periodicals; 620.110285
• DOI: 10.1038/s41524-018-0122-7
• Languages: English
• ISSNs: 2057-3960

Abstract

Abstract Even minute amounts of one solute atom per one million bulk atoms may give rise to qualitative changes in the mechanical response and fracture resistance of modern structural materials. These changes are commonly related to enrichment by several orders of magnitude of the solutes at structural defects in the host lattice. The underlying concept—segregation—is thus fundamental in materials science. To include it in modern strategies of materials design, accurate and realistic computational modelling tools are necessary. However, the enormous number of defect configurations as well as sites solutes can occupy requires models which rely on severe approximations. In the present study we combine a high-throughput study containing more than 1 million data points with machine learning to derive a computationally highly efficient framework which opens the opportunity to model this important mechanism on a routine basis. Grain boundaries: solute-free calculations can predict enrichment Undecorated grain boundaries can yield accurate descriptors to predict isotherms of interfacial energy changes. Liam Huber and others at the Max Planck Institute für Eisenforschung in Düsseldorf developed a framework to compute the segregation energy distributions in aluminium. They first performed a high-throughput study of six solute species segregating at thousands of sites at thirty-eight different types of low and high-symmetry boundaries. They then realistically described the segregation density of states. Using machine learning, they finally identified descriptors which depend only on the local properties of the solute-free grain boundaries, successfully calculating segregation isotherms with significantly less computational effort. Routinely determining segregation isotherms for arbitrary grain boundaries may help us better understand detrimental grain boundary issues, such as embrittlement.