[SY-D4] Machine Learning potentials for modeling iradiation defects in iron and tungsten
Prediction of condensed matter properties requires an accurate description of a material at the atomic scale. Ground state properties of a material are often described well within the Density Functional Theory (DFT) while studying irradiation-induced damage requires a length scale that is pushed beyond ab initio level of theory. Unachievable CPU cost of such calculations have fueled the search for alternatives, accounting for reasonable approximations, which has led to development of various empirical potentials, ranging from pairwise potentials to embedded atom model and tight binding. Although these potentials have been successful in making radiation damage feasible, inconsistency of the results from different potentials is a major shortcoming that hinders conclusive theoretical predictions for such important functional materials as Fe and W.
Here we present a new strategy to achieve machine learning interatomic potentials for metals that approach accuracy of DFT calculations and at the same time preserve a reasonable balance between precision and CPU cost. Targeting to model irradiation-induced defects and plasticity, the potentials are trained on the extensive DFT database that includes EOS, elastic deformation, planar defects (GSF), self-interstitial atoms (SIA), vacancies, and liquid state.
The new potentials for Fe and W are applied to investigate the complex energy landscape of defects under irradiation such as clusters of SIA. We aim to predict the relative stability of large SIA clusters up to nanometric-size, with a particular focus on to the relative stability of the conventional dislocation loops as well as the C15 clusters [1]. The present approach enables us to account for the effect of temperature [2]. Moreover, as a perspective development, the potential will be tested to reproduce high-pressure bcc-hcp transition in Fe.
[1] M.C. Marinica et al. (2012) Phys Rev Lett 108, 025501
[2] T.D. Swinburne, M.C. Marinica (2018) Phys Rev Lett 120, 135503
Here we present a new strategy to achieve machine learning interatomic potentials for metals that approach accuracy of DFT calculations and at the same time preserve a reasonable balance between precision and CPU cost. Targeting to model irradiation-induced defects and plasticity, the potentials are trained on the extensive DFT database that includes EOS, elastic deformation, planar defects (GSF), self-interstitial atoms (SIA), vacancies, and liquid state.
The new potentials for Fe and W are applied to investigate the complex energy landscape of defects under irradiation such as clusters of SIA. We aim to predict the relative stability of large SIA clusters up to nanometric-size, with a particular focus on to the relative stability of the conventional dislocation loops as well as the C15 clusters [1]. The present approach enables us to account for the effect of temperature [2]. Moreover, as a perspective development, the potential will be tested to reproduce high-pressure bcc-hcp transition in Fe.
[1] M.C. Marinica et al. (2012) Phys Rev Lett 108, 025501
[2] T.D. Swinburne, M.C. Marinica (2018) Phys Rev Lett 120, 135503