  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.
