Automated identification of bulk structures, two-dimensional materials, and interfaces using symmetry-based clustering
Abstract 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 classif...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-024-01498-x |
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| _version_ | 1823861798691930112 |
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| author | Thea Denell Lauri Himanen Markus Scheidgen Claudia Draxl |
| author_facet | Thea Denell Lauri Himanen Markus Scheidgen Claudia Draxl |
| author_sort | Thea Denell |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-4786cf33cd1346fcb17dfaa4ad093e73 |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-4786cf33cd1346fcb17dfaa4ad093e732025-02-09T12:46:40ZengNature Portfolionpj Computational Materials2057-39602025-02-011111910.1038/s41524-024-01498-xAutomated identification of bulk structures, two-dimensional materials, and interfaces using symmetry-based clusteringThea Denell0Lauri Himanen1Markus Scheidgen2Claudia Draxl3Physics Department and CSMB, Humboldt-Universität zu BerlinPhysics Department and CSMB, Humboldt-Universität zu BerlinPhysics Department and CSMB, Humboldt-Universität zu BerlinPhysics Department and CSMB, Humboldt-Universität zu BerlinAbstract 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.https://doi.org/10.1038/s41524-024-01498-x |
| spellingShingle | Thea Denell Lauri Himanen Markus Scheidgen Claudia Draxl Automated identification of bulk structures, two-dimensional materials, and interfaces using symmetry-based clustering npj Computational Materials |
| title | Automated identification of bulk structures, two-dimensional materials, and interfaces using symmetry-based clustering |
| title_full | Automated identification of bulk structures, two-dimensional materials, and interfaces using symmetry-based clustering |
| title_fullStr | Automated identification of bulk structures, two-dimensional materials, and interfaces using symmetry-based clustering |
| title_full_unstemmed | Automated identification of bulk structures, two-dimensional materials, and interfaces using symmetry-based clustering |
| title_short | Automated identification of bulk structures, two-dimensional materials, and interfaces using symmetry-based clustering |
| title_sort | automated identification of bulk structures two dimensional materials and interfaces using symmetry based clustering |
| url | https://doi.org/10.1038/s41524-024-01498-x |
| work_keys_str_mv | AT theadenell automatedidentificationofbulkstructurestwodimensionalmaterialsandinterfacesusingsymmetrybasedclustering AT laurihimanen automatedidentificationofbulkstructurestwodimensionalmaterialsandinterfacesusingsymmetrybasedclustering AT markusscheidgen automatedidentificationofbulkstructurestwodimensionalmaterialsandinterfacesusingsymmetrybasedclustering AT claudiadraxl automatedidentificationofbulkstructurestwodimensionalmaterialsandinterfacesusingsymmetrybasedclustering |