Abstract
We present an extension of the `learn on the fly' method to the study of the motion of dislocations in metallic systems, developed with the aim of producing information-efficient force models that can be systematically validated against reference QM calculations. Nye tensor analysis is used to dynamically track the quantum region centered at the core of a dislocation, thus enabling quantum mechanics/molecular mechanics simulations. The technique is used to study the motion of screw dislocations in Ni-Al systems, relevant to plastic deformation in Ni-based alloys, at a variety of temperature/strain conditions. These simulations reveal only a moderate spacing ($\ensuremath{\sim}5\phantom{\rule{0.16em}{0ex}}\AA{}$) between Shockley partial dislocations, at variance with the predictions of traditional molecular dynamics (MD) simulation using interatomic potentials, which yields a much larger spacing in the high stress regime. The discrepancy can be rationalized in terms of the elastic properties of an hcp crystal, which influence the behavior of the stacking fault region between Shockley partial dislocations. The transferability of this technique to more challenging systems is addressed, focusing on the expected accuracy of such calculations. The bcc $\ensuremath{\alpha}\text{\ensuremath{-}}\mathrm{Fe}$ phase is a prime example, as its magnetic properties at the open surfaces make it particularly challenging for embedding-based QM/MM techniques. Our tests reveal that high accuracy can still be obtained at the core of a dislocation, albeit at a significant computational cost for fully converged results. However, we find this cost can be reduced by using a machine learning approach to progressively reduce the rate of expensive QM calculations required during the dynamical simulations, as the size of the QM database increases.
Highlights
The need to produce accurate dynamical representations of chemical processes involving defects in metallic systems has been steadily increasing in recent years
We find that the QM/MM embedding method provides a useful tool for validating potentials, as well as an alternative route to carry out molecular dynamics (MD) simulations when interatomic potentials are not sufficiently accurate
This approach is used for studying the glide of a screw dislocation in γ -Ni, focusing on the local arrangement of atoms at its core and, in particular, on the length of the stacking faulted region separating Shockley partial dislocations
Summary
The need to produce accurate dynamical representations of chemical processes involving defects in metallic systems has been steadily increasing in recent years. The QM region is embedded in a suitable atomistic virtual environment (not the MM region), so that the QM calculation is performed in a periodic system rather than a cluster of atoms, reducing the effect of Friedel oscillations propagating from the free surface [21] These QM/MM approaches provide accurate structural models that can be used for identifying the equilibrium configuration of crystal defects in metals with QM accuracy. The “learn on the fly” (LOTF) scheme [22] is a QM/MM algorithm based on the fitting of a corrective energy function, fitted to reproduce target QM forces in the core region and augmented by a predictor/corrector algorithm for computational efficiency This method has been successfully adopted to simulate a number of problems related to fracture in semiconductor materials, including impurity-driven scattering mechanisms [23] and stress corrosion [24]. Principle achieve accurate prediction of QM forces for the materials and systems presented in this work and could provide appropriate support to the use of LOTF techniques
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