SKM 2023 – wissenschaftliches Programm

Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

MM: Fachverband Metall- und Materialphysik

MM 3: Development of Computational Methods: Evaporation, Growth and Oxidation – Density Functional, Tight Binding

MM 3.1: Vortrag

Montag, 27. März 2023, 10:15–10:30, SCH A 251

Towards machine learning potentials for field evaporation — •Shyam Katnagallu, Joerg Neugebauer, and Christoph Freysoldt — Department of computational materials design, Max Planck Institut für Eisenforschung GmBH, Düsseldorf, Germany.

Field evaporation, an electrostatic field-induced ionization and subsequent evaporation of surface atoms, is the underlying principle of atom probe tomography. Ab initio simulations including 1-10 V/Å fields on metallic slabs have recently shed light [1] on the intricacies of the field evaporation process. These simulations underscore the importance of knowing the path of the evaporating atom prior to complete ionization to improve the spatial resolution of the technique. However, to properly sample the extremely shallow potential energy surface due to the electrostatic field extensive simulations with computationally expensive ab initio accuracy are needed. We, therefore, combine machine learning interatomic potentials with a charge equilibration scheme. To demonstrate the performance and accuracy of our scheme, we validate the Rappe et al charge equilibration model [2] using Hirshfeld decomposed DFT reference charges acquired from (13,5,7) Pt slab under electric fields ranging from (1-4.5 V/Å) and develop potentials for field evaporation in Al. [1]*M. Ashton, A. Mishra, J. Neugebauer, and C. Freysoldt, Ab Initio Description of Bond Breaking in Large Electric Fields, Phys. Rev. Lett. 124, (2020). [2]*A. K. Rappe and W. A. G. Iii, Charge Equilibration for Molecular Dynamics Simulations, J. Phys. Chem 95, 3358 (1991).