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

Abstract

The analysis of defects and defect dynamics in crystalline materials is important for fundamental science and for a wide range of applied engineering. With increasing system size the analysis of molecular-dynamics simulation data becomes non-trivial. Here, we present a workflow for semi-automatic identification and classification of defects in crystalline structures, combining a new approach for defect description with several already existing open-source software packages. Our approach addresses the key challenges posed by the often relatively tiny volume fraction of the modified parts of the sample, thermal motion and the presence of potentially unforeseen atomic configurations (defect types) after irradiation. The local environment of any atom is converted into a rotation-invariant descriptive vector (‘fingerprint’), which can be compared to known defect types and also yields a distance metric suited for classification. Vectors which cannot be associated to known structures indicate new types of defects. As proof-of-concept we apply our method on an iron sample to analyze the defects caused by a collision cascade induced by a 10 keV primary-knock-on-atom. The obtained results are in good agreement with reported literature values. Program summaryProgram Title: Fingerprinting and Visualization Analyzer of Defects (FaVAD).CPC Library link to program files:https://doi.org/10.17632/bmv9kxkzg3.1Developer’s repository link:http://gitlab.mpcdf.mpg.de/NMPP/favad.gitLicensing provisions: GPLv3.Programming language: Python 3, Fortran 90, and C++.Nature of problem: The analysis of damage and damage evolution in crystalline materials is important for fundamental science and for a wide range of applied engineering. Defects in materials on an atomic level are commonly analyzed by Wigner–Seitz or topology Voronoi tessellation based methods. However, these approaches exhibit specific shortcoming, especially at elevated sample temperatures. In order to improve upon that, a more robust and quantifiable identification and classification approach of known as well as of unpredicted defect structures is desirable.Solution method: A fingerprint-like method is proposed to analyze in detail the damage in a material augmented with a probabilistic interpretation. It is based on the calculation of a descriptor vector for each atom in the sample. These vectors represent in a compact form the individual environments of the atoms.For standard types of defects (i.e. interstitial atoms) the corresponding descriptor vectors can be precomputed and used for rapid classification. Unexpected or less common defect types can be identified by applying a principal component analysis to the descriptor vectors. Vacancies in the material are identified by computing the radii of the largest empty spheres which can be embedded into the sample, followed by a thresholding process. This new method is easy to use and requires only modest computational resources.Finally, the classified defects are visualized using the open source software VisIt.Additional comments including restrictions and unusual features: The descriptor vectors are computed using the command line interface of QUIP with the Gaussian Approximation potential (GAP) package. The analysis of the sample is done using Python scripts which make extensive use of the NumPy package. A modified KDTree2 code is employed to calculate the location of single vacancies and voids. The program VisIt is used for the visualization of the classified point defects in the sample. We provide a Dockerfile for automatically creating a portable Docker container which installs all the programs together with a Python script to analyze as an example a damaged iron molecular dynamics sample. A shell script to install the programs locally in a Linux-based server or desktop environment is included also.