Nanoindentation simulations for copper and tungsten with adaptive-precision potentials

Abstract

We perform nanoindentation simulations for both the prototypical face-centered cubic metal copper and the body-centered cubic metal tungsten with a new 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 expensive ACE potential is employed selectively only in regions of the computational cell where large accuracy 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 which is able to describe accurately the angular character of bonding in W due to its half-filled d-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.

16 pages, 16 figures

References in corpus (12)

• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• Machine-learning interatomic potentials for materials science
• Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems
• Automated discovery of a robust interatomic potential for aluminum
• Simple machine-learned interatomic potentials for complex alloys
• Data-driven Material Models for Atomistic Simulation
• Automated free energy calculation from atomistic simulations
• {110} planar faults in strained bcc metals: Origins and implications of a commonly observed artifact of classical potentials
• atomicrex - A general purpose tool for the construction of atomic interaction models
• Adaptive coupling of a deep neural network potential to a classical force field
• Proper Orthogonal Descriptors for Efficient and Accurate Interatomic Potentials
• Adaptive-precision potentials for large-scale atomistic simulations