TY - JOUR

T1 - High-Throughput First-Principles Calculations and Machine Learning of Grain Boundary Segregation in Metals

AU - Scheiber, Daniel

AU - Razumovskiy, Vsevolod I.

AU - Peil, Oleg E.

AU - Romaner, Lorenz

PY - 2024/6/21

Y1 - 2024/6/21

N2 - The segregation of solute elements to defects in metals plays a fundamental role for microstructure evolution and the material performance. However, the available computational data are scattered and inconsistent due to the use of different simulation parameters and methods. A high-throughput study is presented on grain boundary and surface segregation together with their effect on grain boundary embrittlement using a consistent first-principles methodology. The data are evaluated for most technologically relevant metals including Al, Cu, Fe, Mg, Mo, Nb, Ni, Ta, Ti, and W with the majority of the elements from the periodic table treated as segregating elements. Trends among the solute elements are analyzed and explained in terms of phenomenological models and the computed data are compared to the available literature data. The computed first-principles data are used for a machine learning investigation, showing the capabilities for extrapolation from first-principles calculation to the whole periodic table of solutes. The present work allows for comprehensive screening of new alloys with improved interface properties.

AB - The segregation of solute elements to defects in metals plays a fundamental role for microstructure evolution and the material performance. However, the available computational data are scattered and inconsistent due to the use of different simulation parameters and methods. A high-throughput study is presented on grain boundary and surface segregation together with their effect on grain boundary embrittlement using a consistent first-principles methodology. The data are evaluated for most technologically relevant metals including Al, Cu, Fe, Mg, Mo, Nb, Ni, Ta, Ti, and W with the majority of the elements from the periodic table treated as segregating elements. Trends among the solute elements are analyzed and explained in terms of phenomenological models and the computed data are compared to the available literature data. The computed first-principles data are used for a machine learning investigation, showing the capabilities for extrapolation from first-principles calculation to the whole periodic table of solutes. The present work allows for comprehensive screening of new alloys with improved interface properties.

U2 - 10.1002/adem.202400269

DO - 10.1002/adem.202400269

M3 - Article

VL - 26.2024

JO - Advanced engineering materials

JF - Advanced engineering materials

SN - 1527-2648

IS - 19

M1 - 2400269

ER -