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Amin Reiners-Sakic

Publications and source records attributed to Amin Reiners-Sakic.

3 recordsLinked to original sources

DFT and MLIP study of solute segregation to coherent and semi-coherent {\alpha}-Fe/Fe$_3$C interfaces

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 $\alpha$-Fe/Fe$_3$C 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 reproduce the quantum-mechanical predictions. While Cu has the strongest segregation energy of $\approx$ -0.3 eV to the coherent interface among the investigated tramp and trace elements, all of them, As, Cr, Cu, Mo, Ni, P, Sb, and Sn, exhibit significantly more negative segregation values reaching below $\approx$ -1.5 eV in the presence of the misfit dislocation at the semi-coherent interface. The deepest traps are identified in the vicinity of the dislocation core, although the spatial distribution of segregation energies differs markedly among the solute species. The cohesion of the coherent interface is strongly reduced by Sb, Sn, P, and As, and only mildly by Cu, whereas Ni shows a negligible effect, and Cr and Mo slightly enhance cohesion. In contrast, all investigated solutes (except for P) tend to embrittle the semi-coherent interface, with Sn and, especially, Sb having the strongest impact in tensile tests performed in the out-of-plane direction. Abstract shortened for ArXiv.

cond-mat.mtrl-sci

Amending CALPHAD databases using a neural network for predicting mixing enthalpy of liquids

In order to establish the thermodynamic stability of a system, knowledge of its Gibbs free energy is essential. Most often, the Gibbs free energy is predicted within the CALPHAD framework using models employing thermodynamic properties, such as the mixing enthalpy, heat capacity, and activity coefficients. Here, we present a deep-learning approach capable of predicting the mixing enthalpy of liquid phases of binary systems that were not present in the training dataset. Therefore, our model allows for a system-informed enhancement of the thermodynamic description to unknown binary systems based on information present in the available thermodynamic assessment. Thereby, significant experimental efforts in assessing new systems can be spared. We use an open database for steels containing 91 binary systems to generate our initial training (and validation) and amend it with several direct experimental reports. The model is thoroughly tested using different strategies, including a test of its predictive capabilities. The model shows excellent predictive capabilities outside of the training dataset as soon as some data containing species of the predicted system is included in the training dataset. The estimated uncertainty of the model is below 1 kJ/mol for the predicted mixing enthalpy. Subsequently, we used our model to predict the enthalpy of mixing of all binary systems not present in the original database and extracted the Redlich-Kister parameters, which can be readily reintegrated into the thermodynamic database file.

physics.chem-ph

Interstitials as a key ingredient for P segregation to grain boundaries in polycrystalline $\alpha$-Fe

Solute segregation to grain boundaries (GBs) significantly impacts material behavior, with most studies focusing on substitutional solute segregation while neglecting interstitial segregation due to its increased complexity. The site preference, interstitial or substitutional, for P segregation in $\alpha$-Fe still remains under debate. This work investigates both substitutional and interstitial GB segregation in a polycrystalline model using classical interatomic potentials and machine learning. The method is validated with H and Ni, whose segregation behaviors are well understood. For P, we find segregation to both GB site types, with a preference for substitutional sites based on mean segregation energy. However, the abundance of interstitial sites means interstitial segregation also significantly contributes to the GB enrichment with P. This highlights the importance of considering interstitial P segregation alongside substitutional segregation. Additionally, obtaining a representative spectrum of segregation energies is crucial for accurate, experimentally aligned predictions.

cond-mat.mtrl-sci