Computational discovery of extremal microstructure families

Area of Science:

• Materials Science
• Computational Engineering
• Mechanical Engineering

Background:

• Advanced fabrication techniques like additive manufacturing enable the creation of complex engineered materials, known as metamaterials or microstructures.
• These materials offer a wider range of bulk properties than base materials but are challenging to design manually.
• Current design processes for metamaterials with extraordinary properties are typically labor-intensive and performed by hand.

Purpose of the Study:

• To develop an automated computational approach for discovering microstructure families with extremal macroscale properties.
• To efficiently compute the mechanical property space of physically realizable microstructures.
• To enable the generation of new microstructure designs through parameterized templates.

Main Methods:

• Utilizing efficient simulation and sampling techniques to map the property space of microstructures.
• Clustering microstructures with similar topologies into distinct families.
• Extracting parameterized templates from identified families to generate novel designs.

Main Results:

• Demonstrated the computational design of mechanical metamaterials.
• Identified and presented five auxetic microstructure families exhibiting extremal elastic properties.
• Successfully automated the discovery and design process for microstructures.

Conclusions:

• The proposed computational approach facilitates the automated discovery of microstructures with extremal properties.
• This method has the potential for broad application across various physics domains, including thermal, electrical, and magnetic properties.
• Opens avenues for the completely automated discovery of novel materials with tailored functionalities.