Machine learning-based and experimentally validated optimal adhesive fibril 
designs

Son, Donghoon; Liimatainen, Ville; Sitti, Metin

doi:10.1002/smll.202102867

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

Machine learning-based and experimentally validated optimal adhesive fibril designs

MPS-Authors

/persons/resource/persons203145

Son, Donghoon
External Organizations;
Dept. Physical Intelligence, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons259774

Liimatainen, Ville
External Organizations;
Dept. Physical Intelligence, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons140424

Sitti, Metin
External Organizations;
Dept. Physical Intelligence, Max Planck Institute for Intelligent Systems, Max Planck Society;
Dept. of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Son, D., Liimatainen, V., & Sitti, M. (2021). Machine learning-based and experimentally validated optimal adhesive fibril designs. Small, 17(39): 2102867. doi:10.1002/smll.202102867.

Cite as: https://hdl.handle.net/21.11116/0000-0009-6A44-4

Abstract

There is no abstract available