Kavli Affiliate: Wei Gao
| First 5 Authors: Fei Shuang, Kai Liu, Yucheng Ji, Wei Gao, Luca Laurenti
| Summary:
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.
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