Modeling Extensive Defects in Metals through Classical Potential-Guided Sampling and Automated Configuration Reconstruction

Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals, and accurately modeling these extensive defects is crucial for understanding their deformation mechanisms. Existing machine learning interatomic potentials (MLIPs) often fall short in adequately describing these defects, as their significant characteristic sizes exceed the computational limits of first-principles calculations. In this study, we address these challenges by establishing a comprehensive defect genome through empirical interatomic potential-guided sampling. To further enable accurate first-principles calculations on this defect genome, we have developed an automated configuration reconstruction technique. This method transforms defect atomic clusters into periodic configurations through precise atom insertion, utilizing Grand Canonical Monte Carlo simulations. These strategies enable the development of highly accurate and transferable MLIPs for modeling extensive defects in metals. Using body-centered cubic tungsten as a model system, we develop an MLIP that reveals unique plastic mechanisms in simulations of nanoindentation. This framework not only improves the modeling accuracy of extensive defects in crystalline materials but also establishes a robust foundation for further advancement of MLIP development through the strategic use of defect genomes.