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SMILES-driven machine learning for high-throughput investigation of anti-corrosion materials.

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Title: SMILES-driven machine learning for high-throughput investigation of anti-corrosion materials.
Authors: Akrom, Muhamad1, m.akrom@dsn.dinus.ac.id, Al Azies, Harun1, Herowati, Wise1, Sutojo, Totok1, Rustad, Supriadi1, srustad@dsn.dinus.ac.id, Dipojono, Hermawan Kresno1,2, dipojono@itb.ac.id, Kasai, Hideaki3
Source: Chemometrics & Intelligent Laboratory Systems; Aug2025, Vol. 263, pN.PAG-N.PAG, 1p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 185684797
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: SMILES-driven machine learning for high-throughput investigation of anti-corrosion materials.
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  Data: <searchLink fieldCode="JN" term="%22Chemometrics+%26+Intelligent+Laboratory+Systems%22">Chemometrics & Intelligent Laboratory Systems</searchLink>; Aug2025, Vol. 263, pN.PAG-N.PAG, 1p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=185684797
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.chemolab.2025.105441
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
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      – TitleFull: SMILES-driven machine learning for high-throughput investigation of anti-corrosion materials.
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            NameFull: Akrom, Muhamad
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            NameFull: Al Azies, Harun
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            NameFull: Herowati, Wise
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            NameFull: Sutojo, Totok
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            NameFull: Rustad, Supriadi
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            NameFull: Dipojono, Hermawan Kresno
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            – D: 15
              M: 08
              Text: Aug2025
              Type: published
              Y: 2025
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              Value: 263
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            – TitleFull: Chemometrics & Intelligent Laboratory Systems
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