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Rapid Bayesian optimisation for synthesis of short polymer fiber materials

Li, Cheng, Rubín de Celis Leal, David, Rana, Santu, Gupta, Sunil, Sutti, Alessandra, Greenhill, Stewart, Slezak, Teo, Height, Murray and Venkatesh, Svetha 2017, Rapid Bayesian optimisation for synthesis of short polymer fiber materials, Scientific reports, vol. 7, pp. 1-10, doi: 10.1038/s41598-017-05723-0.

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Title Rapid Bayesian optimisation for synthesis of short polymer fiber materials
Author(s) Li, Cheng
Rubín de Celis Leal, David
Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Sutti, AlessandraORCID iD for Sutti, Alessandra orcid.org/0000-0002-1793-3881
Greenhill, StewartORCID iD for Greenhill, Stewart orcid.org/0000-0002-7585-9632
Slezak, Teo
Height, Murray
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Journal name Scientific reports
Volume number 7
Article ID 5683
Start page 1
End page 10
Total pages 10
Publisher Nature Publishing Group
Place of publication London, Eng.
Publication date 2017-07-18
ISSN 2045-2322
Keyword(s) Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
Summary 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.
Language eng
DOI 10.1038/s41598-017-05723-0
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30098043

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.