Ultrahigh Specific Strength by Bayesian Optimization of Carbon Nanolattices

Area of Science:

• Materials Science
• Nanotechnology
• Mechanical Engineering

Background:

• Traditional nanoarchitected materials face limitations in stress distribution and nodal failure.
• Metamaterial design requires advanced structural optimization for superior mechanical performance.

Purpose of the Study:

• To create optimized carbon nanolattices with enhanced specific strength and Young's modulus.
• To investigate the impact of generative design on lattice failure responses and mechanical properties.
• To demonstrate scalable metamaterial fabrication with nanometer precision.

Main Methods:

• Multi-objective Bayesian optimization combined with two-photon polymerization.
• Generative design optimization for lattice topology.
• Pyrolysis of nanolattices to create high-strength carbon with atomic gradients.
• Multi-focus multi-photon polymerization for millimeter-scale fabrication.

Main Results:

• Achieved exceptional specific strength (2.03 MPa m³ kg⁻¹) at low densities (<215 kg m⁻³).
• Demonstrated significant improvements in strength (up to 118%) and Young's modulus (up to 68%) via generative design.
• Created high-strength carbon (94% sp² aromatic) with reduced oxygen impurities.
• Fabricated a millimeter-scalable metamaterial with 18.75 million nanometer-scale lattice cells.

Conclusions:

• Optimized nanostructures exhibit steel-like strength at Styrofoam density, enabling advanced lightweighting.
• The developed materials offer unparalleled capabilities for fuel reduction and contemporary design.
• Bayesian optimization and nanoarchitected pyrolyzed carbon represent a breakthrough in metamaterial design.