Computational prediction of new auxetic materials

Area of Science:

• Materials Science
• Solid-State Physics
• Computational Materials Science

Background:

• Auxetic materials exhibit a negative Poisson's ratio, expanding laterally when stretched, unlike conventional materials.
• Known auxetic materials are often porous foams or artificial structures, with few inorganic polycrystalline examples.
• Discovering new auxetic materials is challenging due to their rarity in solid-state forms.

Purpose of the Study:

• To develop and apply a computational strategy for identifying novel inorganic auxetic materials.
• To screen a large materials database for compounds exhibiting negative Poisson's ratio.
• To identify materials with near-zero Poisson's ratio, termed 'anepirretic materials'.

Main Methods:

• Utilized high-throughput computations and materials databases (Materials Project).
• Employed search algorithms to identify candidate materials based on features correlating with auxetic behavior.
• Calculated Poisson's ratios from the elastic tensor of selected candidate compounds.

Main Results:

• Predicted three previously unidentified homogeneously auxetic inorganic materials.
• Identified several compounds exhibiting a near-zero homogeneous Poisson's ratio (anepirretic materials).
• Demonstrated the efficacy of computational screening for discovering materials with specific mechanical properties.

Conclusions:

• The computational approach successfully identified new inorganic auxetic materials.
• The study expands the known family of auxetic materials beyond porous structures.
• This work provides a pathway for discovering materials with tailored mechanical responses, including auxetic and anepirretic behaviors.