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Prediction of Negative Thermal Expansion in 2D-Materials by Multistep Machine Learning Using Structural Descriptors.

Arko Mohari1, Soumya Mondal1, Debashis Sing Mura1

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Chemistry, an Asian Journal
|January 31, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning model to efficiently predict negative thermal expansion (NTE) in 2D materials. The model accurately identifies 194 new NTE materials, accelerating the discovery of advanced thermal expansion materials.

Keywords:
2D‐materialsartificial neural networkmachine learningmetamaterialsnegative thermal expansionquasi‐harmonic approximation

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Computational Materials Science

Background:

  • Discovering new negative thermal expansion (NTE) metamaterials is experimentally challenging and computationally intensive.
  • Predicting thermal expansion coefficient (TEC) and NTE maxima (αmax) is crucial for materials design.

Purpose of the Study:

  • To develop a machine learning (ML) approach for efficient prediction of NTE maxima and TEC in 2D materials.
  • To rapidly screen a large number of 2D materials for NTE properties.

Main Methods:

  • A multistep machine learning (ML) model was utilized, taking structural and tunable features as input.
  • Predictions were validated against first-principles calculations using the quasi-harmonic approximation (QHA).
  • Blind tests on materials from the 2DMatPedia database confirmed model robustness.

Main Results:

  • The ML model demonstrated high correlation with QHA calculations for predicted target attributes.
  • Out of 234 investigated 2D materials, 194 were identified as exhibiting NTE between 0-1000 K.
  • Key features influencing NTE were identified, guiding future material design.

Conclusions:

  • The developed ML approach offers a systematic and efficient method for screening 2D materials for NTE.
  • This work accelerates the discovery and design of novel 2D NTE materials.
  • Identified features provide valuable insights for tailoring material properties for specific thermal expansion behaviors.