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Rapid Bayesian optimisation for synthesis of short polymer fiber materials.

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.

SUBMITTER: Li C

PROVIDER: S-EPMC5515927 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Rapid Bayesian optimisation for synthesis of short polymer fiber materials.

Li Cheng C Rubín de Celis Leal David D Rana Santu S Gupta Sunil S Sutti Alessandra A Greenhill Stewart S Slezak Teo T Height Murray M Venkatesh Svetha S

Scientific reports 20170718 1

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 proces ...[more]

PMID: 28720869

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