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

DMD Zapiain and M Guziewski and SP Coleman and R Dingreville, JOURNAL OF PHYSICAL CHEMISTRY C, 124, 24809-24821 (2020).

DOI: 10.1021/acs.jpcc.0c07590

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 Sigma 5 < 100 >120 and Sigma 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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