Speaker
Description
Atomistic simulations are the most used theoretical methods to study mechanical properties at nano-scale level. However, the most frequently used molecular dynamics (based on Newton mechanics) is limited by the reliability of available interatomic potentials while the much more precise ab initio methods (based on quantum mechanics) can be performed only for relatively small computational cells and at absolute zero temperature. To overcome these limitations, one can use the ab initio molecular dynamics with on-the-fly machine learning as it is implemented in the current version of the Vienna Ab initio Simulation Package (VASP). This allows to perform molecular dynamics simulations with almost ab initio precision and overcome the limitations of both mentioned approaches.
In this work, we demonstrate how the aforementioned tool is used for segregation study of Sn, P and Ge atoms at selected GBs in bcc iron. In the first stage, segregation characteristics were obtained using small supercells containing up to 100 atoms or medium cells containing approximately 400 atoms and compared with the data obtained from the ab initio simulations. This comparison served as a benchmark of the machine learned force fields (MLFFs) obtained from the VASP on-the-fly machine learning. The comparison shows that the computed segregation energies and other GB characteristics determined via MLFF are very consistent with those obtained from the ab initio simulations. This provides a proof of reliability of the obtained MLFF. After these benchmarks, we employed significantly larger atomistic models capable of describing low impurity concentration at finite temperatures. The received results clarify how simulation cell size might affect the GB characteristics, shed light into temperature influence and provide information about the impurity segregation for many GB types.
