Unbiased Atomistic Predictions of Crystal Dislocation Dynamics using Bayesian Force Fields

TL;DR

This paper introduces a Bayesian machine-learned force field that accurately simulates high-temperature dislocation dynamics in crystals, enabling atomistic insights into phenomena previously computationally infeasible.

Contribution

The work develops a generalizable protocol for creating MLFFs for dislocation kinetics, extending quantum accuracy to mesoscale simulations of crystal deformation.

Findings

Accurate predictions of dislocation mobilities and cross-slip barriers.

Excellent agreement with experimental measurements.

First reliable quantitative determination of dislocation dynamics at high temperatures.

Abstract

Crystal dislocation dynamics, especially at high temperatures, represents a subject where experimental phenomenological input is commonly required, and parameter-free predictions, starting from quantum methods, have been beyond reach. This is especially true for phenomena like stacking faults and dislocation cross-slip, which are computationally intractable with methods like density functional theory, as $\sim 10^5-10^6$ atoms are required to reliably simulate such systems. Hence, this work extends quantum-mechanical accuracy to mesoscopic molecular dynamics simulations and opens unprecedented possibilities in material design for extreme mechanical conditions with direct atomistic insight at the deformation mesoscale. To accomplish this, we construct a Bayesian machine-learned force field (MLFF) from ab initio quantum training data, enabling direct observations of high-temperature and high-stress dislocation dynamics in single-crystalline Cu with atomistic resolution. In doing so, a generalizable training protocol is developed for construction of MLFFs for dislocation kinetics, with wide-ranging applicability to other single element systems and alloys. The resulting FLARE MLFF provides excellent predictions of static bulk elastic properties, stacking fault widths and energies, dynamic evolutions and mobilities of edge and screw dislocations, as well as cross-slip energy barriers for screw dislocations, all of which are compared to available experimental measurements. This work marks the first reliable quantitative determination of dislocation mobilities and cross-slip barriers, demonstrating a substantial advancement over previous empirical and machine learned force field models.