Automated identification of bulk structures, two-dimensional materials, and interfaces using symmetry-based clustering

• Published in: npj computational materials Vol. 11; no. 1; pp. 25 - 9
• Main Authors: Denell, Thea, Himanen, Lauri, Scheidgen, Markus, Draxl, Claudia
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 06.02.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/301/1034/1037; 639/766/25; Algorithms; Atomic structure; Automation; Characterization and Evaluation of Materials; Chemistry and Materials Science; Classification; Clustering; Computational Intelligence; Machine learning; Materials information; Materials Science; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematical Modeling and Industrial Mathematics; Python; Symmetry; Theoretical; Two dimensional materials
• ISSN: 2057-3960; 2057-3960
• DOI: 10.1038/s41524-024-01498-x

With the rapidly increasing amount of materials data being generated in a variety of projects, efficient and accurate classification of atomistic structures is essential. A current barrier to effective database queries lies in the often ambiguous, inconsistent, or completely missing classification of existing data, highlighting the need for standardized, automated, and verifiable classification methods. This work proposes a robust solution for identifying and classifying a wide spectrum of materials through an iterative technique, called symmetry-based clustering (SBC). Because SBC is not a machine learning-based method, it requires no prior training. Instead, it identifies clusters in atomistic systems by automatically recognizing common unit cells. We demonstrate the potential of SBC to provide automated, reliable classification and to reveal well-known symmetry properties of various materials. Even noisy systems are shown to be classifiable, showing the suitability of our algorithm for real-world data applications. The software implementation is provided in the open-source Python package, MatID, exploiting synergies with popular atomic-structure manipulation libraries and extending the accessibility of those libraries through the NOMAD platform.