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Inferring Comonomer Content Using Crystaf: Uncertainty Analysis
Author(s) -
Takeh Arsia,
Shanbhag Sachin
Publication year - 2014
Publication title -
macromolecular theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.37
H-Index - 56
eISSN - 1521-3919
pISSN - 1022-1344
DOI - 10.1002/mats.201400034
Subject(s) - comonomer , inference , computer science , content (measure theory) , domain (mathematical analysis) , statistical inference , copolymer , algorithm , statistics , mathematics , artificial intelligence , chemistry , mathematical analysis , organic chemistry , polymer
We estimate the comonomer content in random copolymers, through the use of semi‐empirical models. We extend the model of Anantawaraskul et al. by expanding the number of model parameters from 4 to 9. Using available data on well‐characterized ethylene/1‐hexene copolymers, we randomly select a subset to train the model, and regress model parameters. We test the ability of the parametrized model to infer comonomer content on the rest of the data. We quantify the predictive ability by exploring the effect of the quantity and quality of the training data. The accuracy and precision of the inference improve as the amount of training data increases, and as datasets span the domain more evenly.