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Abstract. In this paper we introduce a stacking-fault based model to understand the energetics of formation of the nanolamellar-based metal carbide and nitride structures. The model is able to reproduce the cohesive energies of the stacking fault phases from Density Functional Theory calculations by fitting the energy of different stacking sequences of metal layers. The model demonstrates that the first and second nearest metal-metal neighbor interactions and the nearest metal-carbon/nitrogen interaction are the dominant terms in determining the cohesive energy of these structures. The model further demonstrates that above a metal to non-metal ratio of 75%, there is no energetic favorability for the stacking faults to form a long-range ordered structure. The model's applicability is demonstrated using the Ta-C system as its case study from which we report that the interfacial energy between ζ-Ta 4 C 3 and TaC or Ta 2 C is negligible. Our results suggest that the closed packed planes of these phases should be aligned and that precipitated phases should be thin, which is in agreement with experiments.

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Predicting the properties of grain boundaries poses a challenge because of the complex relationships between structural and chemical attributes both at the atomic and continuum scales. Grain boundary systems are typically characterized by parameters used to classify local atomic arrangements in order to extract features such as grain boundary energy or grain boundary strength. The present work utilizes a combination of high-throughput atomistic simulations, macroscopic and microscopic descriptors, and machine-learning techniques to characterize the energy and strength of silicon carbide grain boundaries. A diverse data set of symmetric tilt and twist grain boundaries are described using macroscopic metrics such as misorientation, the alignment of critical low-index planes, and the Schmid factor, but also in terms of microscopic metrics, by quantifying the local atomic structure and chemistry at the interface. These descriptors are used to create random-forest regression models, allowing for their relative importance to the grain boundary energy and decohesion stress to be better understood. Results show that while the energetics of the grain boundary were best described using the microscopic descriptors, the ability of the macroscopic descriptors to reasonably predict grain boundaries with low energy suggests a link between the crystallographic orientation and the resultant atomic structure that forms at the grain boundary within this regime. For grain boundary strength, neither microscopic nor macroscopic descriptors were able to fully capture the response individually. However, when both descriptor sets were utilized, the decohesion stress of the grain boundary could be accurately predicted. These results highlight the importance of considering both macroscopic and microscopic factors when constructing constitutive models for grain boundary systems, which has significant implications for both understanding the fundamental mechanisms at work and the ability to bridge length scales.

The local atomic structure, local chemistry, and stoichiometry of grain boundaries control in part the strength and fracture toughness of silicon carbide components. The predictions of the structure and properties of these grain boundaries are generally limited to their ground-state configurations. We investigated the tensile strength behavior of metastable grain boundaries in silicon carbide using high-throughput atomistic simulations combined with machine learning techniques. We analyzed and compared the ∑5 ⟨100⟩{120} and ∑9 ⟨110⟩{122} tilt grain boundary metastable configurations to identify structural and chemical attributes that dominate their tensile strength. We characterized these metastable grain boundaries using a set of microscopic descriptors representing the local grain boundary atomic structure and the local grain boundary stoichiometry and chemical-bound types. We used a boosted regression tree surrogate model for the successful prediction of metastable grain boundary strength as a function of these descriptors. Our results show that the tensile strength of generic (i.e., any random grain boundary from the entire grain boundary population), metastable grain boundaries is primarily dominated by the grain boundary excess free volume, closely followed by the type of structure composing the boundary and the amount of C–C bonds. The 5% strongest metastable grain boundaries have particular characteristics with a low amount of free volume and the highest density of C–C bonds. Our results reveal that the 5% strongest and weakest metastable grain boundaries are most sensitive to the local stoichiometry, regardless of the local atomic structure composing the grain boundary as compared to any other generic metastable grain boundaries. We show that the strongest and weakest metastable grain boundary configurations can be identified as specific regions in a low-dimensional-representation space of their microscopic descriptors. Taken together, these findings showcase the effectiveness and validity of using a low-dimensional representation of the grain boundary structure and machine-learned surrogate models to rapidly assess metastable grain boundary strength without the need to perform actual tensile simulations.

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