A Neural Network Interatomic Potential for Crystal Plasticity Mechanisms in Nanoindentation Simulation: The Case of Pure Molybdenum
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Numerical investigations of nano-mechanical testing for metals and alloys require accurate interatomic potentials that may predict configurational energies and interatomic forces, consistent with ab initio calculations. In this work, we investigate crystalline molybdenum (Mo), a viable candidate for extreme environments, functioning at elevated temperatures. Mechanical properties of Mo, such as nanoindentation hardness, display non-trivial temperature dependence that requires further validation and deeper understanding, beyond classical force fields methods. In this work, we create a Neural-network interatomic potential (NNIP) for nanoindentation of pure crystalline Mo to investigate mechanisms of dislocation nucleation and evolution, at multiple temperatures, up to 1000K. Compared to common machine learned potentials (MLPs) in the literature and by employing a similarity measure between an indented sample and MLP datasets, we found very relevant configurations to add to common MLP datasets that were missed before. Elastic constants, dislocation densities, strain maps and slip traces as a function of indentation depth of the system are compared with embedded atom method (EAM) potentials and the advantages and limitations of NNIPs over traditional potentials are reported.
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