Atomistic and machine learning studies of solute segregation in metastable grain boundaries

Abstract

The interaction of alloying elements with grain boundaries (GBs) influences many phenomena, such as microstructural evolution and transport. While GB solute segregation has been the subject of active research in recent years, most studies focus on ground-state GB structures, i.e., lowest energy GBs. The impact of GB metastability on solute segregation remains poorly understood. Herein, we leverage atomistic simulations to generate metastable structures for a series of [001] and [110] symmetric tilt GBs in a model Al–Mg system and quantify Mg segregation to individual sites within these boundaries. Our results show large variations in the atomic Voronoi volume due to GB metastability, which are found to influence the segregation energy. The atomistic data are then used to train a Gaussian Process machine learning model, which provides a probabilistic description of the GB segregation energy in terms of the local atomic environment. In broad terms, our approach extends existing GB segregation models by accounting for variability due to GB metastability, where the segregation energy is treated as a distribution rather than a single-valued quantity.

Efficient Machine Learning of Solute Segregation Energy Based on Physics-informed Features

“…Atomic volume per se already gives an RMSE of 0.056 eV (Fig. 2a), more accurate than similar modeling of an AlMg system that also used atomic volume as the only feature but with training data obtained from bicrystals 22 . Adding a disorder factor into the feature set can reduce the RMSE to 0.0436 eV (Fig.…”

“…Atomic volume suitable for the solute atom size tends to have high binding energy and is favorable for segregation, while that too small/large tends to decrease the binding energy and is not favorable for segregation. It is therefore highly expected and has been con rmed that the atomic volume is strongly relevant to the segregation energy 22 . Atomic volume only tells information about the nearest neighboring atoms.…”

“…Machine learning data should be representative of the sample space, a universal set of all possible segregation sites in this work. While the segregation could happen anywhere inside a GB network common in a realistic alloy, the literature usually used bicrystals when preparing the data 13,14,22 , where the possible sites are limited within the single GB inside the con guration. Even though many such con gurations can provide a variety of GBs containing various segregation sites, the obtained data is yet a su cient sampling for a common GB network due to the exclusion of triple junctions that are more preferable segregation sites 23 .…”

Metastable grain boundaries: the roles of structural and chemical disorders in their energetics, non-equilibrium kinetic evolution, and mechanical behaviors

“…Mahmood et al [119] developed a Gaussian Process Regression (GPR) model aimed at delivering a probabilistic characterization of GB segregation energy. Their emphasis was on exploring the connection between GB segregation energy ∆E and the excess atomic volume ∆V V0 within metastable GBs.…”

Computed Entropy Spectra for Grain Boundary Segregation in Polycrystals

“…While the simplicity of the McLean isotherm has enabled its usage in a wide range of situations [24][25][26][27][28] , it neglects the atomistic nature of GBs, which involves a broad range of local atomic environments [29][30][31][32][33][34] . Collapsing the true spectral nature of GB sites to single-value effective quantities in Eq.…”