FaVAD: A software workflow for characterization and visualizing of defects in crystalline structures

Modelling the primary damage in Fe and W: influence of the short-range interactions on the cascade properties: Part 2 – multivariate multiple linear regression analysis of displacement cascades

“…Our work thus shows that the methodology used until now by many groups to identify two defects as belonging to the same cluster, which relies only on the distance between the defects, is not sufficient to properly characterize the cluster. Indeed, beyond the number of point defects, the shape (loops, C15 and all kinds of imperfect structures) is also very important, raising the question of the classification of defects as shown for instance in [34,35]. Here, we describe in detail our methodology to be able to compare with the work reported in the literature, but the purpose here is not to propose a new methodology which would require a significant amount of statistics and which is out of the scope of the present study.…”

Temperature effects on the point defects formation in [111] W by neutron induced collision cascade

“…In our previous work, we applied interatomic potentials based on the Gaussian Approximation Potential (GAP) framework to numerically model the damage in crystalline materials due to irradiation in a fusion reactor [12,19]. MD simulations were analyzed by a recently developed workflow for semi-automatic identification and classification of defects in crystalline structures and reported results are in good agreement with experimental data [20]. Therefore, the goal of the present work is to better understand the thermal activated mechanisms for material damage in crystalline W samples which is commonly used in experiments for irradiation in a fusion reactor.…”

Computational study of crystal defect formation in Mo by a machine learning molecular dynamics potential

“…This is followed by a computation of the nearest neighbor distance between the position of the damaged sample atoms and the sampling grid points. Points where the distance to the nearest atom exceeds a given threshold describe the spatial volume of the identified vacancy [16,17].…”

“…Once the DVs of all the atoms of the damaged material are computed, we calculate the distance between the two corresponding DVs, d = d ( q i , q j ) to the atomic local environment of a defect free and thermalized material sample [18,16] as follows…”

“…Our paper is organized as follows: in section 2 we briefly discuss the theory to develop the machine learned (ML) potential [15] for Mo, and the software workflow for fingerprinting and visualizing defects in damaged crystal structures (FaVAD) [16,17] that is applied to quantify and classify the damage in Mo samples [18]. Our results for the total number of points defects, Frenkel pairs and atomic displacement are presented in section 3.…”