Scientific Reports (Jul 2017)

Rapid Bayesian optimisation for synthesis of short polymer fiber materials

  • Cheng Li,
  • David Rubín de Celis Leal,
  • Santu Rana,
  • Sunil Gupta,
  • Alessandra Sutti,
  • Stewart Greenhill,
  • Teo Slezak,
  • Murray Height,
  • Svetha Venkatesh

DOI
https://doi.org/10.1038/s41598-017-05723-0
Journal volume & issue
Vol. 7, no. 1
pp. 1 – 10

Abstract

Read online

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.