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Exploring negative thermal expansion materials with bulk framework structures and their relevant scaling

Yu Cai1,2, Chunyan Wang1,2,3, Huanli Yuan3

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Summary

Machine learning efficiently identifies over 1000 potential negative thermal expansion (NTE) materials from databases. This approach predicts coefficients and temperature ranges, aiding the design of novel NTE materials.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Machine Learning in Materials Discovery

Background:

  • Experimental discovery of negative thermal expansion (NTE) materials is challenging.
  • Machine learning (ML) offers a data-driven approach to accelerate materials discovery.
  • Existing material databases can be leveraged for identifying novel NTE candidates.

Purpose of the Study:

  • To employ a multi-step machine learning method for identifying potential NTE materials.
  • To predict the coefficients of negative thermal expansion (CNTE) and operational temperature ranges.
  • To establish relationships between material properties and NTE behavior for design guidance.

Main Methods:

  • Utilized a multi-step machine learning approach with data augmentation and cross-validation.
  • Screened materials from the Inorganic Crystal Structure Database (ICSD) and other sources.
  • Employed first-principles calculations with quasi-harmonic approximation (QHA) for validation.

Main Results:

  • Identified approximately 1000 candidate materials (oxides, fluorides, cyanides) with bulk framework structures.
  • Predicted CNTE values and temperature ranges for the identified materials.
  • Approximately 57 materials showed a 100% predicted probability for NTE.
  • ML predictions showed good agreement with first-principles calculations.
  • Established three universal relationships for CNTE based on average electronegativity, porosity, and temperature range.

Conclusions:

  • The ML approach is effective for discovering and predicting NTE materials.
  • Identified critical values and relationships can guide the design of new NTE materials.
  • This work expands the library of known and potential NTE materials.