Essential Step toward Mining Big Polymer Data: PolyName2Structure, Mapping Polymer Names to Structures
2020
Advances
in polymer science have made polymers essential in our
everyday life and have yielded unprecedented quantities of data over
the past several decades. However, it is still challenging and inefficient
to organize such scattered and accumulated “big data”
in a text format through mass journals, patents, and web pages due
to the complexity and ambiguity of polymer representations. In this
paper, we report the first automated framework, PolyName2Structure
(PN2S), which is able to convert various polymer name representations
to their corresponding polymer structures. In PN2S, machine learning
models were built to predict the polymerization pathway, identify
the reacting group(s), and generate repeating units after polymerization.
This PN2S system achieved over 90% accuracy when applied to polymer
names listed in a commercial catalog, embodying the first step toward
resolving the complexity of the data structure for polymers by building
a practical model that enables text mining of structural polymer information.
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