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ČERNÝ, M. ŠESTÁK, P.
Originální název
Segregation of Phosphorus and Silicon at the Grain Boundary in Bcc Iron via Machine-Learned Force Fields
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
The study of the effects of impurity on grain boundaries is a critical aspect of materials science, particularly when it comes to understanding and controlling the properties of materials for specific applications. One of the related key issues is the segregation preference of impurity atoms in the grain boundary region. In this paper, we employed the on-the-fly machine learning to generate force fields, which were subsequently used to calculate the segregation energies of phosphorus and silicon in bcc iron containing the n-ary sumation 5(310)[001] grain boundary. The generated force fields were successfully benchmarked using ab initio data. Our further calculations considered impurity atoms at a number of possible interstitial and substitutional segregation sites. Our predictions of the preferred sites agree with the experimental observations. Planar concentration of impurity atoms affects the segregation energy and, moreover, can change the preferred segregation sites.
Klíčová slova
DFT calculations; machine learning; grain boundaries; impurity segregation
Autoři
ČERNÝ, M.; ŠESTÁK, P.
Vydáno
12. 1. 2024
Nakladatel
MDPI
Místo
BASEL
ISSN
2073-4352
Periodikum
Crystals
Ročník
14
Číslo
1
Stát
Švýcarská konfederace
Strany počet
11
URL
https://www.mdpi.com/2073-4352/14/1/74
Plný text v Digitální knihovně
http://hdl.handle.net/11012/245477
BibTex
@article{BUT188350, author="Miroslav {Černý} and Petr {Šesták}", title="Segregation of Phosphorus and Silicon at the Grain Boundary in Bcc Iron via Machine-Learned Force Fields", journal="Crystals", year="2024", volume="14", number="1", pages="11", doi="10.3390/cryst14010074", issn="2073-4352", url="https://www.mdpi.com/2073-4352/14/1/74" }