2023
DOI: 10.1002/adem.202300048
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Lattice Metamaterials with Mesoscale Motifs: Exploration of Property Charts by Bayesian Optimization

Abstract: Architectural materials at mesoscale open new opportunitiesfor the design of materials with unique combinations of properties. [1][2][3] One of the subclasses of this kind of material is lattice (meta)materials. Classical lattice materials have a mesh-like structure generated by translation in space of an elementary cell that comprises several, most commonly identical, elements, such as thin bars or rods. [4,5] It was found that materials with this type of inner architecture while having a low density, exhibit… Show more

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Cited by 5 publications
(2 citation statements)
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“…By combining the KMC protocol with active learning, the configuration‐property linkage can be understood for further optimization of SEI configurations, and the generation of SEI configuration and its corresponding property prediction can be made faster than a simulation method with numerous computations. [ 14,15 ]…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…By combining the KMC protocol with active learning, the configuration‐property linkage can be understood for further optimization of SEI configurations, and the generation of SEI configuration and its corresponding property prediction can be made faster than a simulation method with numerous computations. [ 14,15 ]…”
Section: Introductionmentioning
confidence: 99%
“…By combining the KMC protocol with active learning, the configuration-property linkage can be understood for further optimization of SEI configurations, and the generation of SEI configuration and its corresponding property prediction can be made faster than a simulation method with numerous computations. [14,15] The conventional trial-and-error-based optimization of materials starts from known material configurations and requires human effort to direct the material optimization for better performance. In contrast, the inverse material design allows the definition of the system's desired performance targets to determine the material configuration that meets these targets.…”
Section: Introductionmentioning
confidence: 99%