Data-driven, or machine learning (ML), approaches have become viable alternatives to semiempirical methods to construct interatomic potentials, due to their capacity to accurately interpolate and extrapolate from first-principles simulations if the training database and descriptor representation of atomic structures are carefully chosen. Here, we present highly accurate interatomic potentials suitable for the study of dislocations, point defects, and their clusters in bcc iron and tungsten, constructed using a linear or quadratic input-output mapping from descriptor space. The proposed quadratic formulation, called quadratic noise ML, differs from previous approaches, being strongly preconditioned by the linear solution. The developed potentials are compared to a wide range of existing ML and semiempirical potentials, and are shown to have sufficient accuracy to distinguish changes in the exchange-correlation functional or pseudopotential in the underlying reference data, while retaining excellent transferability. The flexibility of the underlying approach is able to target properties almost unattainable by traditional methods, such as the negative divacancy binding energy in W or the shape and the magnitude of the Peierls barrier of the $\frac{1}{2}\langle 111\rangle$ screw dislocation in both metals. We also show how the developed potentials can be used to target important observables that require large time-and-space scales unattainable with first-principles methods, though we emphasize the importance of thoughtful database design and degrees of nonlinearity of the descriptor space to achieve the appropriate passage of information to large-scale calculations. As a demonstration, we perform direct atomistic calculations of the relative stability of $\frac{1}{2}\langle 111\rangle$ dislocations loops and three-dimensional C15 clusters in Fe and find the crossover between the formation energies of the two classes of interstitial defects occurs at around 40 self-interstitial atoms. We also compute the kink-pair formation energy of the $\frac{1}{2}\langle 111\rangle$ screw dislocation in Fe and W, finding good agreement with density functional theory informed line tension models that indirectly measure those quantities. Finally, we exploit the excellent finite-temperature properties to compute vacancy formation free energies with full anharmonicity in thermal vibrations. The presented potentials thus open up many avenues for systematic investigation of free-energy landscape of defects with ab initio accuracy.

This work revises the concept of defects in crystalline solids and proposes a universal strategy for their characterization at the atomic scale using outlier detection based on statistical distances. The proposed strategy provides a generic measure that describes the distortion score of local atomic environments. This score facilitates automatic defect localization and enables a stratified description of defects, which allows to distinguish the zones with different levels of distortion within the structure. This work proposes applications for advanced materials modelling ranging from the surrogate concept for the energy per atom to the relevant information selection for evaluation of energy barriers from the mean force. Moreover, this concept can serve for design of robust interatomic machine learning potentials and high-throughput analysis of their databases. The proposed definition of defects opens up many perspectives for materials design and characterization, promoting thereby the development of novel techniques in materials science.

This work represents a numerical study of the thermal activation for dislocation glide of the [100](010) slip system in MgSiO₃ post-perovskite (Mg-ppv) at 120 GPa. We propose an approach based on a one-dimensional line tension model in conjunction with atomic-scale calculations. In this model, the key parameters, namely, the line tension and the Peierls barrier, are obtained from density functional theory calculations. We find a Peierls stress σ_p = 2.1 GPa and a line tension Γ = 9.2 eV/Å, which lead to a kink-pair enthalpy (under zero stress) of 2.69 eV. These values confirm that this slip system bears a very low lattice friction because it vanishes for temperatures above approximately 500 K under mantle conditions. In the Earth’s mantle, high-pressure Mg-ppv silicate is thus expected to become as ductile as ferropericlase. These results confirm the hypothesis of a weak layer in the D″ layer where Mg-ppv is present. Easy glide along [100](010) suggests strong preferred orientations with (010) planes aligned. Highly mobile [100] dislocations are also likely to respond to stresses related to seismic waves, leading to energy dissipation and strong attenuation.