Nanoindentation simulations for copper and tungsten with adaptive-precision potentials

We perform nanoindentation simulations for both the prototypical face-centered cubic metal copper and the body-centered cubic metal tungsten with an adaptive-precision description of interaction potentials including different accuracy and computational costs. We combine both a computationally efficient embedded atom method (EAM) potential and a precise but computationally less efficient machine learning potential based on the atomic cluster expansion (ACE) into an adaptive precision (AP) potential tailored for the nanoindentation. The numerically more expensive ACE potential is employed selectively only in regions of the computational cell where high precision is required. The comparison with pure EAM and pure ACE simulations shows that for Cu, all potentials yield similar dislocation morphologies under the indenter with only small quantitative differences. In contrast, markedly different plasticity mechanisms are observed for W in simulations performed with the central-force EAM potential compared to results obtained using the ACE potential. ACE is able to describe accurately the angular character of bonding, which is in W due to its half-filled <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mrow><a:mi>d</a:mi></a:mrow></a:math> band. All ACE-specific mechanisms are reproduced in the AP nanoindentation simulations, however, with a significant speedup of 20–30 times compared to the pure ACE simulations. Hence, the AP potential overcomes the performance gap between the precise ACE and the fast EAM potential by combining the advantages of both potentials.