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Title:

Machine Learning Approach to Analyze the Surface Properties of Biological Materials

Document type:
Zeitschriftenaufsatz
Author(s):
Rickert, C.A.; Hayta, E.N.; Selle, D.M.; Kouroudis, I.; Harth, M.; Gagliardi, A.; Lieleg, O.
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...     »
Journal title:
ACS Biomater. Sci. Eng. 7, 9, 4614–4625 2021-08
Year:
2021
Year / month:
2021-08
Quarter:
3. Quartal
Month:
Aug
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 Publictions
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