Strength through defects: A novel Bayesian approach for the optimization of architected materials

We use a previously unexplored Bayesian optimization framework, evolutionary Monte Carlo sampling, to systematically design the arrangement of defects in an architected microlattice to maximize its strain energy density before undergoing catastrophic failure. Our algorithm searches a design space with billions of 4 x 4 x 5 3D lattices, yet it finds the global optimum with only 250 cost function evaluations. Our optimum has a normalized strain energy density 12,464 times greater than its commonly studied defect-free counterpart. Traditional optimization is inefficient for this microlattice because (i) the design space has discrete, qualitative parameter states as input variables, (ii) the cost function is computationally expensive, and (iii) the design space is large. Our proposed framework is useful for architected materials and for many optimization problems in science and elucidates how defects can enhance the mechanical performance of architected materials.

Automated summary

The study explored the use of a Bayesian optimization framework with evolutionary Monte Carlo sampling to systematically design defect arrangements in a structured microlattice. The proposed algorithm efficiently found the global optimum in a large design space, resulting in a normalized strain energy density significantly higher than its defect-free counterpart. Traditional optimization methods were deemed inefficient for this specific microlattice design.