Grain boundary strengthening in ZrB2 by segregation of W: Atomistic simulations with deep learning potential

Interaction between grain boundaries and impurities usually leads to significant altering of material properties. Understanding the composition-structure-property relationship of grain boundaries is a key avenue for tailoring and designing high performance materials. In this work, we studied segregation of W into ZrB₂ grain boundaries by a hybrid method combining Monte Carlo (MC) and molecular dynamics (MD), and examined the effects of segregation on grain boundary strengths by MD tensile testing with a fitted machine learning potential. It is found that W prefers grain boundary sites with local compression strains due to its smaller size compared to Zr. Rich segregation patterns (including monolayer, off-center bilayer, and other complex patterns); segregation induced grain boundary structure reconstruction; and order-disorder like segregation pattern transformation are discovered. Strong segregation tendency of W into ZrB₂ grain boundaries and significant improvements on grain boundary strengths are certified, which guarantees outstanding high temperature performance of ZrB₂-based UHTCs.