Crack-tip deformation mechanism: Active learning interatomic potential!

The prediction of atomistic fracture mechanisms in body-centred cubic (bcc) iron is essential for understanding its semi-brittle nature. Current research suggests that the interplay between thermal activation of screw dislocations and crack-tip dislocation emission dominates the brittle-to-ductile transition in BCC metals. Due to the limitation of computational power, DFT is not able to simulate the atomic-scale crack-tip extension. Classical molecular dynamics (MD) with different EAM interatomic potentials yield different crack-tip deformation mechanisms, which contradicts each other. This study proposes an active learning approach to extract crack-tip configurations, applicable across various machine learning frameworks and materials, enabling first-principles accuracy in predicting atomic-scale crack-tip deformation mechanism. Professor Francesco Maresca and PhD student Lei Zhang at the University of Groningen developed a Gaussian approximation potential with near DFT accuracy, revealing that brittle fracture is the dominating mechanism in single-crystal iron with pre-existing cracks at low temperatures. The research demonstrates that the accuracy of machine learning potentials can be improved through specially designed database configurations, and active learning is significantly efficient than manually adding relevant configurations. By comparing the MD predicted fracture toughness with experiments, the study showed that even at low temperatures (77K), the influence of dislocations on fracture toughness cannot be ignored. The research emphasizes the importance of multiscale simulations in predicting fracture toughness, offering new insights for engineering materials using multiscale simulation methods.