User: Guest  Login
Title:

Machine Learning Approach to Analyze the Surface Properties of Biological Materials

Document type:
Zeitschriftenaufsatz
Author(s):
Rickert, Carolin A.; Hayta, Elif N.; Selle, Daniel M.; Kouroudis, Ioannis; Harth, Milan; Gagliardi, Alessio; Lieleg, Oliver
Abstract:
Similar to how CRISPR has revolutionized the field of molecular biology, machine learning may drastically boost research in the area of materials science. Machine learning is a fast-evolving method that allows for analyzing big data and unveiling correlations that otherwise would remain undiscovered. It may hold invaluable potential to engineer novel functional materials with desired properties, a field, which is currently limited by time-consuming trial and error approaches and our limited unde...     »
Keywords:
deep learning; hydrophobicity; supervised learning; topography; wettability
Dewey Decimal Classification:
500 Naturwissenschaften
Journal title:
ACS Biomaterials Science & Engineering
Year:
2021
Journal volume:
7
Journal issue:
9
Pages contribution:
4614-4625
Covered by:
Scopus
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.1021/acsbiomaterials.1c00869
WWW:
https://pubs.acs.org/doi/abs/10.1021/acsbiomaterials.1c00869
Publisher:
American Chemical Society (ACS)
E-ISSN:
2373-98782373-9878
Date of publication:
20.08.2021
TUM Institution:
Fachgebiet für Biomechanik, MW
 BibTeX