DFT and MLIP study of solute segregation to coherent and semi-coherent {\alpha}-Fe/Fe$_3$C interfaces
Amin Reiners-Sakic, Ronald Schnitzer, David Holec

TL;DR
This study uses novel machine learning interatomic potentials to investigate solute segregation at alpha-Fe/Fe3C interfaces, revealing how different elements influence interface cohesion and embrittlement, especially near dislocations.
Contribution
It introduces and benchmarks universal machine learning interatomic potentials for modeling solute segregation at complex semi-coherent interfaces in iron-carbon systems.
Findings
Cu shows the strongest segregation energy among studied elements.
Solutes like Sb, Sn, P, and As significantly reduce interface cohesion.
Most solutes tend to embrittle the semi-coherent interface, especially near dislocations.
Abstract
Solute segregation to interfaces significantly impacts material behavior. A large majority of theoretical works focus on grain boundaries and coherent interfaces. Studies on semi-coherent interfaces are usually prohibited by the structural complexity, yielding models beyond the practical capability of density functional theory (DFT), or chemical complexity, restricted by the availability of (classical) interatomic potentials. This work investigates solute segregation to the coherent and semi-coherent -Fe/FeC interface in pearlite and its effect on mechanical properties using novel universal machine learning interatomic potentials (uMLIPs). DFT calculated solution enthalpies, segregation energetics, and changes in cohesion at the coherent interface are used to benchmark several state-of-the-art uMLIPs. We find that the GRACE-2L-OAM and GRACE-2L-OMAT models most accurately…
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Taxonomy
TopicsMachine Learning in Materials Science · Microstructure and mechanical properties · Microstructure and Mechanical Properties of Steels
