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Volumn 46, Issue 3, 2009, Pages 729-755

Recursive neural networks prediction of glass transition temperature from monomer structure: An application to acrylic and methacrylic polymers

Author keywords

Cheminformatics; Glass transition temperature; QSPR; Recursive neural networks; Stereogularity

Indexed keywords


EID: 70349484405     PISSN: 02599791     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10910-009-9547-z     Document Type: Article
Times cited : (16)

References (39)
  • 20
    • 35748945928 scopus 로고    scopus 로고
    • Recursive neural networks for quantitative structure-property relationship analysis of polymers
    • SimosTheodore and MaroulisGeorge (Eds.), Leiden: Brill Academic Publishers
    • Duce C., Micheli A., Solaro R., Starita A., Tinè M.R.: Recursive neural networks for quantitative structure-property relationship analysis of polymers. In: Theodore, Simos, George, Maroulis (eds) Lecture series on computer and computational sciences, vol. 4., pp. 1546-1549. Brill Academic Publishers, Leiden (2005)
    • (2005) Lecture Series on Computer and Computational Sciences , vol.4 , pp. 1546-1549
    • Duce, C.1    Micheli, A.2    Solaro, R.3    Starita, A.4    Tinè, M.R.5
  • 35
    • 0004220172 scopus 로고
    • 2nd edn., Oxford University, New York
    • Stevens M.P.: Polymer chemistry, 2nd edn. pp. 80. Oxford University, New York (1990)
    • (1990) Polymer Chemistry , pp. 80
    • Stevens, M.P.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.