This paper discusses computational methods for identifying atomic structures in large-scale atomistic simulations of crystalline materials. The authors compare existing techniques such as Common Neighbor Analysis (CNA), Centrosymmetry Parameter (CSP), Bond Order Analysis, Bond Angle Analysis, and Voronoi Analysis. They also introduce two new methods: Adaptive Common Neighbor Analysis (a-CNA) and Neighbor Distance Analysis (NDA). These methods aim to classify atomic structures, particularly in complex environments like grain boundaries. The paper emphasizes the importance of identifying crystal defects in simulations, as they significantly influence material properties. Traditional simulation models do not explicitly track defects, so post-processing is required to analyze simulation data. The methods discussed aim to assign structural types to atoms based on their local environments, enabling visualization and quantification of crystalline phases and defects. CNA is a widely used method that analyzes the topology of bonds between atoms. It is effective for distinguishing between different crystal structures. The authors propose an extension of CNA, a-CNA, to improve its performance in multi-phase systems. NDA is a new method that uses a more complex signature to identify a wider range of atomic arrangements, particularly in complex environments like grain boundaries. The paper also discusses the limitations of existing methods, such as sensitivity to atomic displacements and the need for a threshold to distinguish between different structures. The authors provide a benchmarking implementation of all discussed algorithms, with source code available for reference. The paper concludes that while existing methods are useful, new approaches like a-CNA and NDA offer improved performance in identifying complex atomic structures.This paper discusses computational methods for identifying atomic structures in large-scale atomistic simulations of crystalline materials. The authors compare existing techniques such as Common Neighbor Analysis (CNA), Centrosymmetry Parameter (CSP), Bond Order Analysis, Bond Angle Analysis, and Voronoi Analysis. They also introduce two new methods: Adaptive Common Neighbor Analysis (a-CNA) and Neighbor Distance Analysis (NDA). These methods aim to classify atomic structures, particularly in complex environments like grain boundaries. The paper emphasizes the importance of identifying crystal defects in simulations, as they significantly influence material properties. Traditional simulation models do not explicitly track defects, so post-processing is required to analyze simulation data. The methods discussed aim to assign structural types to atoms based on their local environments, enabling visualization and quantification of crystalline phases and defects. CNA is a widely used method that analyzes the topology of bonds between atoms. It is effective for distinguishing between different crystal structures. The authors propose an extension of CNA, a-CNA, to improve its performance in multi-phase systems. NDA is a new method that uses a more complex signature to identify a wider range of atomic arrangements, particularly in complex environments like grain boundaries. The paper also discusses the limitations of existing methods, such as sensitivity to atomic displacements and the need for a threshold to distinguish between different structures. The authors provide a benchmarking implementation of all discussed algorithms, with source code available for reference. The paper concludes that while existing methods are useful, new approaches like a-CNA and NDA offer improved performance in identifying complex atomic structures.