Strength through defects: A novel Bayesian approach for the optimization of architected materials
| Autor: | Philip Marcus, Maria Farsari, Haris Moazam Sheikh, George Flamourakis, Costas P. Grigoropoulos, Zacharias Vangelatos, Victor Z. Lopez |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Multidisciplinary
Computer science Materials Science Bayesian probability Bayesian optimization SciAdv r-articles Sampling (statistics) Evolutionary monte carlo Applied Sciences and Engineering ComputingMethodologies_DOCUMENTANDTEXTPROCESSING Physical and Materials Sciences Algorithm ComputingMilieux_MISCELLANEOUS Research Article |
| Zdroj: | Science advances, vol 7, iss 41 Science Advances |
| Popis: | Description A substantial improvement in the strength of a material is made with a new optimization method applicable to many science problems. 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 × 4 × 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. |
| Databáze: | OpenAIRE |
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