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.
@article{arxiv.1802.05841,
title = {Rapid Bayesian optimisation for synthesis of short polymer fiber materials},
author = {Cheng Li and David Rubin de Celis Leal and Santu Rana and Sunil Gupta and Alessandra Sutti and Stewart Greenhill and Teo Slezak and Murray Height and Svetha Venkatesh},
journal= {arXiv preprint arXiv:1802.05841},
year = {2018}
}