Characterizing the Tensile Strength of Metastable Grain Boundaries in Silicon Carbide Using Machine Learning

Abstract

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.

A Multi-Scale Approach for Phase Field Modeling of Ultra-Hard Ceramic Composites

“…Figure 7 a shows the resultant distribution of fracture energies from the 50 grain boundaries orientations considered for this work. This distribution is in excellent agreement with both the experimentally determined range of fracture energies for polycrystalline SiC [ 88 ] as well as past computational work on SiC grain boundaries [ 87 , 89 ]. Roughly half of the values within the distribution are greater than that of the plane within SiC (2.3 J/m ), which is the preferred cleavage plane and fracture energy of the micro-crystalline SiC ( Section 3.1.2 ).…”

Microscopic and Macroscopic Characterization of Grain Boundary Energy and Strength in Silicon Carbide via Machine-Learning Techniques

“…While both of these perspectives produce useful information, it has been hypothesized that a combination of both macroscopic and microscopic descriptors is necessary to achieve an accurate prediction of a grain boundary’s properties. , Therefore, by considering these descriptor sets individually and together, it is possible to begin to decouple the effects of the two length scales. This allows for important inferences to be made with regard to the uncertainty within the developed relations, and, in conjunction with studies on metastable states of grain boundaries, variability in properties to be accounted for both across and within different crystallographic orientations. The process of length scale bridging and multiscale modeling does place limitations on the descriptors that can be utilized when attempting to create constitutive relations, as they should be compatible with the inputs available to the higher length scale model.…”

Examination of computed aluminum grain boundary structures and interface energies that span the 5D space of crystallographic character

“…Characterizing microscopic degrees of freedom is a significant challenge, with considerable efforts focused on atomic structures generally [98][99][100][101][102][103] and GBs specifically [12][13][14][15][16][17][18][19][20][21][22]. In this work no new structural characterizations or machine learning methods are employed.…”

“…The microscopic degrees of freedom of a GB are defined at the most basic level by the positions of each atom, where N atoms would have 3N degrees of freedom. Various metrics have been developed to describe the atomic structure of GBs in simpler terms, including the structural unit model [8][9][10], polyhedral unit model [11], and more recently by various collections of local atomic environments [12][13][14][15][16][17][18][19][20][21][22].…”