Accelerated viscosity measurements of polymer solutions using high throughput experimentation and machine learning

Abstract

In this study, machine learning is used in conjunction with a proxy experiment to relate to high fidelity empirical data to measure and predict viscosity of materials in a rapid, and high throughput fashion. This paper details both the proxy and high-fidelity experiments focusing only on viscometry and how computation is used to find a relationship between the proxy and high-fidelity experiments. This method is time efficient and economical in the long run, only requiring initial efforts to setup a base standard for future predictive work.