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Rapid Bayesian optimisation for synthesis of short polymer fiber materials

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journal contribution
posted on 2017-07-18, 00:00 authored by C Li, D Rubín de Celis Leal, Santu RanaSantu Rana, Sunil GuptaSunil Gupta, Alessandra SuttiAlessandra Sutti, Stewart GreenhillStewart Greenhill, Teo Slezak, M Height, Svetha VenkateshSvetha Venkatesh
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.

History

Journal

Scientific reports

Volume

7

Article number

5683

Pagination

1 - 10

Publisher

Nature Publishing Group

Location

London, Eng.

eISSN

2045-2322

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2017, The Authors

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