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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月6日

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  • Thea Denell,
  • Lauri Himanen,
  • Markus Scheidgen,
  • Claudia Draxl

摘要

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.