On the detection and classification of material defects in crystalline solids after energetic particle impact simulations

TL;DR

This paper introduces a computationally efficient, rotation-invariant fingerprint method for detecting and classifying defects in crystalline solids after energetic particle impacts, demonstrated on tungsten simulations.

Contribution

The paper presents a novel, easy-to-use descriptor vector approach for defect analysis that is largely independent of material and temperature, validated on tungsten irradiation simulations.

Findings

Deuterated tungsten shows more defects than pristine tungsten after irradiation.

The method effectively identifies regular lattice atoms and defect types like interstitials and vacancies.

PCA reveals previously overlooked defect structures.

Abstract

We present a fingerprint-like method to analyze material defects after energetic particle irradiation by computing a rotation invariant descriptor vector for each atom of a given sample. For ordered solids this new method is easy to use, does not require extreme computational resources, and is largely independent of the sample material and sample temperature. As illustration we applied the method to molecular dynamics simulations of deuterated and pristine tungsten lattices at 300 K using a primary knock-on atom (PKA) of 1 keV with different velocity directions to emulate a neutron bombardment process. The number of W atoms, that are affected after the collision cascade, have been quantified with the presented approach. At first atoms at regular lattice positions as well as common defect types like interstitials and vacancies have been identified using precomputed descriptor vectors. A principal component analysis (PCA) is used to identify previously overlooked defect types and to derive the corresponding local atomic structure. A comparison of the irradiation effects for deuterated and pristine tungsten samples revealed that deuterated samples exhibit consistently more defects than pristine ones.