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Machine learning analysis of a large set of homopolymers to predict glass transition temperatures.

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Title: Machine learning analysis of a large set of homopolymers to predict glass transition temperatures.
Authors: Casanola-Martin, Gerardo M.1 (AUTHOR), Karuth, Anas1 (AUTHOR), Pham-The, Hai2 (AUTHOR), González-Díaz, Humbert3,4,5 (AUTHOR), Webster, Dean C.1 (AUTHOR), Rasulev, Bakhtiyor1 (AUTHOR) bakhtiyor.rasulev@ndsu.edu
Source: Communications Chemistry. 10/2/2024, Vol. 7 Issue 1, p1-9. 9p.
Database: Academic Search Ultimate
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  Data: Machine learning analysis of a large set of homopolymers to predict glass transition temperatures.
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  Data: <searchLink fieldCode="JN" term="%22Communications+Chemistry%22">Communications Chemistry</searchLink>. 10/2/2024, Vol. 7 Issue 1, p1-9. 9p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=180038285
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        Value: 10.1038/s42004-024-01305-0
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              Text: 10/2/2024
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