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This study introduces an automated computational framework to discover new charge density wave (CDW) materials for nanoelectronics. The method predicts easily exfoliable CDW materials, offering guidelines for experimentalists.

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Area of Science:

  • Condensed Matter Physics
  • Materials Science
  • Computational Materials Science

Background:

  • Charge density wave (CDW) materials are crucial for nanoelectronics due to their resistivity switching properties.
  • The limited availability of CDW materials hinders their practical application.
  • Identifying new CDW materials requires efficient and scalable methods.

Purpose of the Study:

  • To develop a fully automated high-throughput computational framework for identifying charge density wave (CDW) phases.
  • To discover novel, easily exfoliable CDW materials for nanoelectronic applications.
  • To provide insights into the electronic structures and properties of predicted CDW materials.

Main Methods:

  • Combining first-principles calculations with unsupervised machine learning for structure searching.
  • Developing an automated framework to identify CDW phases from unit cells exhibiting Kohn anomalies.
  • High-throughput screening of materials to predict new CDW phases.

Main Results:

  • The framework successfully rediscovers known CDW phases.
  • Predicts 30 new materials and 114 new CDW phases, many of which are easily exfoliable.
  • Detailed analysis of ZrTiSe4 reveals Fermi surface nesting leading to semiconducting gap opening in its CDW phase.

Conclusions:

  • The developed computational framework accelerates the discovery of novel CDW materials.
  • The predicted materials offer promising candidates for next-generation nanoelectronic devices.
  • Findings provide valuable guidance for experimental synthesis and characterization of CDW materials.