Publication detail

Segregation of Phosphorus and Silicon at the Grain Boundary in Bcc Iron via Machine-Learned Force Fields

ČERNÝ, M. ŠESTÁK, P.

Original Title

Segregation of Phosphorus and Silicon at the Grain Boundary in Bcc Iron via Machine-Learned Force Fields

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

DFT calculations; machine learning; grain boundaries; impurity segregation

Authors

ČERNÝ, M.; ŠESTÁK, P.

Released

12. 1. 2024

Publisher

MDPI

Location

BASEL

ISBN

2073-4352

Periodical

Crystals

Year of study

14

Number

1

State

Swiss Confederation

Pages count

11

URL

https://www.mdpi.com/2073-4352/14/1/74

Full text in the Digital Library

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"
}