2021/06/28 by Michael Engel, Engel, Michael · 1 citation
Chemistry · Materials Science · Mathematics · Physics and Astronomy · #Algorithm #Chemical Physics (physics.chem-ph) #Chemistry #Computational Physics (physics.comp-ph) #Computer science #Crystal (programming language) #Crystal structure #Crystallography #Enzyme Structure and Function #FOS: Physical sciences #Geometry #Group (periodic table) #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Mathematics #Mesoscale and Nanoscale Physics (cond-mat.mes-hall) #Order (exchange) #Particle (ecology) #Physics #Point (geometry) #Soft Condensed Matter (cond-mat.soft) #Statistical physics #Symmetry (geometry) #X-ray Diffraction in Crystallography #cond-mat.mes-hall #cond-mat.mtrl-sci #cond-mat.soft #physics.chem-ph #physics.comp-ph
paper · pdf · doi:10.48550/arxiv.2106.14846
published in arXiv (Cornell University) (Cornell University) · 8 pages
arxiv created 2021/06/28 · openalex publication_date 2021/06/28 · arxiv updated 2021/06/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
A routine crystallography technique, crystal structure analysis, is rarely performed in computational condensed matter research. The lack of methods to identify and characterize crystal structures reliably in particle simulation data complicates the comparison of simulation outcomes to experiment and the discovery of new materials. Algorithms are sought that not only classify local structure but also analyze the type and degree of crystallographic order. Here, we develop an algorithm that analyzes point group symmetry directly from particle coordinates. The algorithm operates on functions defined on the surface of the sphere, such as the bond orientational order diagram. Other use cases are the orientation of crystals and adoption as generalized order parameters for detecting the appearance of order as well as following its development.