journal article·2023-07Open access

Lattice Metamaterials with Mesoscale Motifs: Exploration of Property Charts by Bayesian Optimization

Roman Kulagin, Patrick Reiser, Kyryl Truskovskyi, Arnd Koeppe, Yan Beygelzimer, Yuri Estrin, Pascal Friederich, Peter Gumbsch

Advanced Engineering Materials· Volume 25· Issue 13

Abstract

Through the current work, the usefulness of the concept of architectured rod lattices based on unit cell motifs designed at mesoscale is demonstrated. Specifically, 2D triangular lattices with unit cells containing different numbers of rods are considered. Combinations of rods of two different types provide the lattices explored with a greater complexity and versatility. For mesocells with a large number of variable parameters, it is virtually impossible to calculate the entire set of the points mapping the material onto its property space, as the volume of calculations would be gigantic. The number of possible motifs increases exponentially with the number of rods. Herein, the lattice metamaterials with mesoscale motifs are investigated with the focus on their elastic properties by combining machine learning techniques (specifically, Bayesian optimization) with finite element computations. The proposed approach made it possible to construct property charts illustrating the evolution of the boundary of the elastic compliance tensor of lattice metamaterials with an increase in the number of rods of the mesocell when a full‐factor experiment would not be possible.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Cellular and Composite Structures · Acoustic Wave Phenomena Research · Advanced Materials and Mechanics

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute of Nanotechnology Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen Germany

Institute of Theoretical Informatics Karlsruhe Institute of Technology Engler-Bunte-Ring 8 76131 Karlsruhe Germany

Borealis AI Montreal QC H2S 3H1 Canada

Institute for Applied Materials (IAM-MMS) Karlsruhe Institute of Technology Straße am Forum 7 76131 Karlsruhe Germany

Donetsk Institute for Physics and Engineering named after A.A. Galkin National Academy of Sciences of Ukraine Nauki Ave., 46 03028 Kyiv Ukraine

Department of Materials Science and Engineering Monash University 22 Alliance Lane Clayton 3800 Australia

Department of Mechanical Engineering The University of Western Australia Crawley 6009 Australia

Institute for Applied Materials Karlsruhe Institute of Technology Straße am Forum 7 76131 Karlsruhe Germany

Fraunhofer Institute for Mechanics of Materials IWM Wöhlerstraße 11 79108 Freiburg Germany

Funding

Deutsche Forschungsgemeinschaft

References (39)

Show source-provided references
1. 10.1016/j.scriptamat.2012.04.03310.1016/j.scriptamat.2012.04.033 ↗
2. 10.1007/978-3-030-11942-310.1007/978-3-030-11942-3 ↗
3. 10.1080/21663831.2021.196190810.1080/21663831.2021.1961908 ↗
4. 10.1098/rsta.2005.167810.1098/rsta.2005.1678 ↗
5. 10.1098/rspa.2010.021510.1098/rspa.2010.0215 ↗
6. 10.1016/j.mser.2021.10060610.1016/j.mser.2021.100606 ↗
7. 10.1002/anie.20170469510.1002/anie.201704695 ↗
8. 10.1002/adma.20170185010.1002/adma.201701850 ↗
9. 10.1002/adma.20160061010.1002/adma.201600610 ↗
10. 10.1016/j.eml.2019.10055310.1016/j.eml.2019.100553 ↗
11. 10.1038/s41563-022-01269-310.1038/s41563-022-01269-3 ↗
12. 10.1002/adem.20180086410.1002/adem.201800864 ↗
13. 10.1126/science.abn145910.1126/science.abn1459 ↗
14. 10.1038/s41524-022-00938-w10.1038/s41524-022-00938-w ↗
15. 10.1002/adem.20200099110.1002/adem.202000991 ↗
16. 10.1002/adem.20220138510.1002/adem.202201385 ↗
17. 10.1002/adem.20200106910.1002/adem.202001069 ↗
18. 10.1016/B978-1-85617-663-7.00011-410.1016/B978-1-85617-663-7.00011-4 ↗
19. 10.3389/fmats.2020.0005110.3389/fmats.2020.00051 ↗
20. 10.3389/fmats.2022.82181710.3389/fmats.2022.821817 ↗
21. Bartle R. G. · 2000
22. Kolmogorov A. N. · 1999
23. 10.1007/s00366-019-00910-110.1007/s00366-019-00910-1 ↗
24. 10.1007/s00158-021-03068-x10.1007/s00158-021-03068-x ↗
25. 10.1016/j.aiopen.2021.01.00110.1016/j.aiopen.2021.01.001 ↗
26. K.He X.Zhang S.Ren J.Sun inProc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit IEEE Las Vegas NV2016 p.770 https://doi.org/10.1109/CVPR.2016.90.10.1109/CVPR.2016.90 ↗
27. 10.1109/TIE.2022.316181210.1109/TIE.2022.3161812 ↗
28. 10.1109/TMC.2022.317481610.1109/TMC.2022.3174816 ↗
29. Huang W. · Assoc. Comput. Machin. · 2022 · 22 · 1
30. M.Mirza S.Osindero(Preprint) arXiv:1411.1784 November2014 https://doi.org/10.48550/arXiv.1411.1784.
31. Nogueira F. · 2014
32. 10.5334/dsj-2021-00810.5334/dsj-2021-008 ↗
33. R.Kulagin P.Reiser K.Truskovskyi A.Koeppe Y.Beygelzimer Y.Estrin P.Friederich P.Gumbsch [Dataset]2022 https://doi.org/10.5281/zenodo.7516301.https://kadi4mat.iam-cms.kit.edu/collections/907.
34. Christensen M. R. · 2012
35. 10.1038/ncomms513010.1038/ncomms5130 ↗
36. Mao Y. · Sci. Adv. · 2020 · 6
37. 10.3390/ma1408182210.3390/ma14081822 ↗
38. 10.1002/adem.20190126610.1002/adem.201901266 ↗
39. 10.1073/pnas.211150511910.1073/pnas.2111505119 ↗