Rapid Bayesian optimisation for synthesis of short polymer fiber materials

TL;DR

This paper presents a rapid Bayesian optimization method that leverages machine learning to efficiently guide the synthesis process of short polymer fibers, balancing multiple objectives and reducing experimental complexity.

Contribution

It introduces an iterative Bayesian optimization approach tailored for polymer fiber synthesis, integrating qualitative and quantitative goals to accelerate process development.

Findings

Efficiently directed synthesis process for short polymer fibers

Reduced experimental iterations through machine learning optimization

Demonstrated effectiveness on a novel fluid processing platform

Abstract

The discovery of processes for the synthesis of new materials involves many decisions about process design, operation, and material properties. Experimentation is crucial but as complexity increases, exploration of variables can become impractical using traditional combinatorial approaches. We describe an iterative method which uses machine learning to optimise process development, incorporating multiple qualitative and quantitative objectives. We demonstrate the method with a novel fluid processing platform for synthesis of short polymer fibers, and show how the synthesis process can be efficiently directed to achieve material and process objectives.