| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Integration between Modeling and Experiments for Crystalline Metals: From Atomistic to Macroscopic Scales IV
|
| Presentation Title |
Hybrid Ab Initio-machine Learning Simulation of Dislocation-defect Interactions |
| Author(s) |
Petr Grigorev, Alexandra Goryaeva, James Kermode, Mihai-Cosmin Marinica, Thomas Swinburne |
| On-Site Speaker (Planned) |
Thomas Swinburne |
| Abstract Scope |
Calculations of dislocation-defect interactions require system sizes at or beyond ab initio limits. Current estimates thus have extrapolation or finite size errors that are very challenging to quantify. Hybrid methods offer a solution, embedding small ab initio simulations in an empirical medium. However, current implementations can only match mild elastic deformations at the ab initio boundary. We describe a robust method to employ linear-in-descriptor machine learning potentials as a highly flexible embedding medium, precisely matching core properties to allow dislocations to cross the ab initio boundary in fully three dimensional defect geometries. Investigating helium and vacancy segregation to edge and screw dislocations in tungsten, we find long-range relaxations qualitatively change impurity-induced core reconstructions compared to those in short periodic supercells. Our approach opens a vast range of mechanisms to ab initio investigation and provides new reference data for interatomic potentials.
preprint: arXiv:2111.11262
code: https://github.com/marseille-matmol/LML-retrain/ |