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Context <p>This study investigates the orthorhombic polymorph of ScF<sub>3</sub> (<i>C222</i>) under hydrostatic pressure, revealing negative linear compressibility (NLC) along the <i>b</i>-axis. First-principles calculations show anisotropic expansion (<i>b</i>-axis) and contraction (<i>a</i>-, <i>c</i>-axes), with compressibility coefficients quantified up to 40 GPa. The findings advance the understanding of pressure-induced behaviors in ScF<sub>3</sub> and its potential for functional materials design.</p> Methods <p>First-principles calculations were performed using density functional theory (DFT) within the generalized gradient approximation (GGA) framework as implemented in CASTEP. Lattice parameters and elastic properties were computed under varying pressure conditions. The Pascal program was used to determine compressibility coefficients and analyze NLC behavior. Computational settings, including energy cutoffs, k-point sampling, and convergence criteria, were optimized for accuracy and stability.</p>

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Exploring negative linear compressibility in the orthorhombic polymorph of ScF3 space group C222 under high pressure

  • 期刊论文

  • 发表日期：

    2025年8月12日

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  • Houda El Hyani,
  • Haris Habib,
  • Li Gong

摘要

Context

This study investigates the orthorhombic polymorph of ScF3 (C222) under hydrostatic pressure, revealing negative linear compressibility (NLC) along the b-axis. First-principles calculations show anisotropic expansion (b-axis) and contraction (a-, c-axes), with compressibility coefficients quantified up to 40 GPa. The findings advance the understanding of pressure-induced behaviors in ScF3 and its potential for functional materials design.

Methods

First-principles calculations were performed using density functional theory (DFT) within the generalized gradient approximation (GGA) framework as implemented in CASTEP. Lattice parameters and elastic properties were computed under varying pressure conditions. The Pascal program was used to determine compressibility coefficients and analyze NLC behavior. Computational settings, including energy cutoffs, k-point sampling, and convergence criteria, were optimized for accuracy and stability.