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Titel:

Machine Learning Approach to Analyze the Surface Properties of Biological Materials

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
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...     »
Zeitschriftentitel:
ACS Biomater. Sci. Eng. 7, 9, 4614–4625 2021-08
Jahr:
2021
Jahr / Monat:
2021-08
Quartal:
3. Quartal
Monat:
Aug
Sprache:
en
Volltext / DOI:
doi:10.1021/acsbiomaterials.1c00869
WWW:
https://pubs.acs.org/doi/abs/10.1021/acsbiomaterials.1c00869
Verlag / Institution:
American Chemical Society ACS Publictions
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