Predicting Crystallization Tendency of Polymers Using Multifidelity Information Fusion and Machine Learning.

The Journal of Physical Chemistry. B
Shruti VenkatramRampi Ramprasad

Abstract

The degree of crystallinity of a polymer is a critical parameter that controls a variety of polymer properties. A high degree of crystallinity is associated with excellent mechanical properties crucial for high-performing applications like composites. Low crystallinity promotes ion and gas mobility critical for battery and membrane applications. Experimental determination of the crystallinity for new polymers is time and cost intensive. A data-driven machine learning-based method capable of rapidly predicting the crystallinity could counter these disadvantages and be used to screen polymers for a myriad of applications in a fast, inexpensive fashion. In this work, we developed the first-of-its-kind, data-driven machine learning model to predict the most-likely polymer crystallinity trained on experimental data and theoretical group contribution methods. Since polymer data under consistent processing conditions are unavailable, we tackled process variability by using the "most-likely" polymer values which we refer to as the polymer's tendency to crystallize. Experimental data for polymers' tendency to crystallize is limited by number and diversity, and to tackle this, we augmented experimentation-based data with data using group...Continue Reading

Citations

Apr 22, 2021·Soft Matter·Paul S Clegg
Aug 17, 2021·Soft Matter·Kianoosh SattariJian Lin
Nov 6, 2020·The Journal of Physical Chemistry. a·Andrew L FergusonJim Pfaendtner
Nov 6, 2020·The Journal of Physical Chemistry. B·Andrew L FergusonJim Pfaendtner
Jan 25, 2022·The Journal of Physical Chemistry. B·Karteek K BejagamGhanshyam Pilania

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